cd /tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363 && python3 -c " from pptx import Presentation from pptx.util import Inches, Pt import json prs = Presentation('Dr. Prachi.pptx') print(f'Slides: {len(prs.slides)}') print(f'Slide width: {prs.slide_width.inches} inches') print(f'Slide height: {prs.slide_height.inches} inches') for i, slide in enumerate(prs.slides, 1): print(f'\n=== SLIDE {i} ===') print(f'Layout: {slide.slide_layout.name}') for shape in slide.shapes: print(f' Shape: {shape.shape_type}, name={shape.name}, left={round(shape.left/914400,2)}, top={round(shape.top/914400,2)}, width={round(shape.width/914400,2)}, height={round(shape.height/914400,2)}') if shape.has_text_frame: for para in shape.text_frame.paragraphs: text = para.text.strip() if text: print(f' TEXT: {text[:120]}') for run in para.runs: if run.font.size: print(f' font_size={run.font.size.pt}, bold={run.font.bold}, color={run.font.color.rgb if run.font.color and run.font.color.type else None}') "

cd /tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363 && python3 -c " import pdfplumber with pdfplumber.open('Thesis final.pdf') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages[:8], 1): text = page.extract_text() if text: print(f'\n=== PAGE {i} ===') print(text[:1500]) "

cd /tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363 && python3 -c " from pptx import Presentation from pptx.util import Inches, Pt from pptx.dml.color import RGBColor import pptx.oxml.ns as ns prs = Presentation('Dr. Prachi.pptx') print(f'Slides: {len(prs.slides)}') print(f'Slide size: {prs.slide_width.inches} x {prs.slide_height.inches}') for i, slide in enumerate(prs.slides, 1): print(f'\n=== SLIDE {i} (Layout: {slide.slide_layout.name}) ===') # background bg = slide.background fill = bg.fill print(f' BG fill type: {fill.type}') for shape in slide.shapes: print(f' Shape [{shape.shape_type}] name={shape.name!r} pos=({round(shape.left/914400,3)},{round(shape.top/914400,3)}) size=({round(shape.width/914400,3)},{round(shape.height/914400,3)})') if hasattr(shape, 'fill'): try: sf = shape.fill print(f' fill_type={sf.type}') if sf.type and sf.type.name == 'SOLID': print(f' fill_color={sf.fore_color.rgb}') except: pass if shape.has_text_frame: tf = shape.text_frame for j, para in enumerate(tf.paragraphs): text = para.text.strip() if text: al = para.alignment print(f' Para[{j}] align={al}: {text[:100]}') for run in para.runs: try: clr = run.font.color.rgb if run.font.color and str(run.font.color.type) == 'RGB (1)' else 'scheme/inherited' except: clr = 'N/A' print(f' run: size={run.font.size.pt if run.font.size else None} bold={run.font.bold} color={clr} text={run.text[:60]!r}') " 2>&1 | head -200

cd /tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363 && python3 -c " import pdfplumber with pdfplumber.open('Thesis final.pdf') as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages[8:30], 9): text = page.extract_text() if text: print(f'\n=== PAGE {i} ===') print(text[:1200]) " 2>&1

cd /tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363 && python3 -c " import pdfplumber with pdfplumber.open('Thesis final.pdf') as pdf: # Get methods, results, conclusion sections - pages 30-88 for i, page in enumerate(pdf.pages[30:88], 31): text = page.extract_text() if text: print(f'\n=== PAGE {i} ===') print(text[:1000]) " 2>&1

cd /tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363 && python3 -c " from pptx import Presentation from pptx.util import Pt import lxml.etree as etree prs = Presentation('Dr. Prachi.pptx') # Get theme colors theme = prs.slide_master.theme_color_map print('Theme element:') # Get the theme XML slide_master = prs.slide_master theme_elem = slide_master._element.find('.//{http://schemas.openxmlformats.org/drawingml/2006/main}theme') if theme_elem is not None: print(etree.tostring(theme_elem, pretty_print=True).decode()[:3000]) # Check slide master background bg = slide_master.background print(f'Master BG fill type: {bg.fill.type}') # Check the slide master for fonts txStyles = slide_master._element.find('.//{http://schemas.openxmlformats.org/presentationml/2006/main}txStyles') if txStyles is not None: print('txStyles found') print(etree.tostring(txStyles, pretty_print=True).decode()[:2000]) "

mkdir -p /tmp/workspace/thesis-ppt && python /tmp/skills/pptx/scripts/office/unpack.py "/tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363/Dr. Prachi.pptx" /tmp/workspace/thesis-ppt/unpacked/ 2>&1

ls /tmp/workspace/thesis-ppt/unpacked/ppt/slides/ | head -20

cat > /tmp/workspace/thesis-ppt/build_thesis_ppt.py << 'PYEOF' """ Build thesis PPTX based on Dr. Prachi reference file style. Thesis: Effect of AEDs on Cognitive Functions in Paediatric Epilepsy """ import copy, shutil, os from pptx import Presentation from pptx.util import Inches, Pt, Emu from pptx.dml.color import RGBColor from pptx.enum.text import PP_ALIGN from pptx.oxml.ns import qn import lxml.etree as etree # Use the Dr. Prachi file as base template (preserves theme/background/fonts) src = "/tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363/Dr. Prachi.pptx" out = "/tmp/workspace/thesis-ppt/Thesis_AED_Cognitive_Paediatric.pptx" prs = Presentation(src) # ------- Helper functions ------- def clear_slide(slide): """Remove all shapes from a slide.""" sp_tree = slide.shapes._spTree for sp in list(sp_tree): tag = sp.tag.split('}')[-1] if '}' in sp.tag else sp.tag if tag not in ('grpSpPr', 'sp', 'pic', 'graphicFrame', 'cxnSp', 'grpSp'): continue sp_tree.remove(sp) def add_textbox(slide, left, top, width, height, text, font_size=18, bold=False, color=None, align=PP_ALIGN.LEFT, wrap=True, italic=False): txBox = slide.shapes.add_textbox( Inches(left), Inches(top), Inches(width), Inches(height)) tf = txBox.text_frame tf.word_wrap = wrap p = tf.paragraphs[0] p.alignment = align run = p.add_run() run.text = text run.font.size = Pt(font_size) run.font.bold = bold run.font.italic = italic if color: run.font.color.rgb = RGBColor(*color) return txBox def add_heading_box(slide, title, left=0.3, top=0.25, width=9.4, height=0.65, font_size=26, bg_color=(30, 74, 135), text_color=(255,255,255)): """Styled heading box like Dr. Prachi.""" box = slide.shapes.add_shape( 1, # MSO_SHAPE_TYPE.RECTANGLE Inches(left), Inches(top), Inches(width), Inches(height)) fill = box.fill fill.solid() fill.fore_color.rgb = RGBColor(*bg_color) box.line.fill.background() tf = box.text_frame tf.word_wrap = False p = tf.paragraphs[0] p.alignment = PP_ALIGN.CENTER run = p.add_run() run.text = title run.font.size = Pt(font_size) run.font.bold = True run.font.color.rgb = RGBColor(*text_color) return box def add_bullet_content(slide, bullets, left=0.3, top=1.1, width=9.4, height=5.8, font_size=17, heading=None, heading_color=(30,74,135)): """Add a text box with bullet points.""" txBox = slide.shapes.add_textbox( Inches(left), Inches(top), Inches(width), Inches(height)) tf = txBox.text_frame tf.word_wrap = True first = True if heading: p = tf.paragraphs[0] if first else tf.add_paragraph() first = False p.alignment = PP_ALIGN.LEFT run = p.add_run() run.text = heading run.font.size = Pt(font_size + 1) run.font.bold = True run.font.color.rgb = RGBColor(*heading_color) for bullet in bullets: if first: p = tf.paragraphs[0] first = False else: p = tf.add_paragraph() p.alignment = PP_ALIGN.JUSTIFY # Check if sub-bullet if isinstance(bullet, tuple): indent, text = bullet else: indent, text = 0, bullet p.level = indent run = p.add_run() run.text = text run.font.size = Pt(font_size - indent) return txBox def add_table_slide(slide, headers, rows, left=0.3, top=1.1, width=9.4, col_widths=None, font_size=14): """Add a formatted table.""" from pptx.util import Inches, Pt from pptx.dml.color import RGBColor rows_count = len(rows) + 1 cols_count = len(headers) total_width = Inches(width) if col_widths: col_w = [Inches(w) for w in col_widths] else: col_w = [total_width // cols_count] * cols_count table = slide.shapes.add_table( rows_count, cols_count, Inches(left), Inches(top), total_width, Inches(rows_count * 0.45 + 0.1) ).table # Set column widths for i, w in enumerate(col_w): table.columns[i].width = w # Header row for ci, hdr in enumerate(headers): cell = table.cell(0, ci) cell.text = hdr cell.fill.solid() cell.fill.fore_color.rgb = RGBColor(30, 74, 135) p = cell.text_frame.paragraphs[0] p.alignment = PP_ALIGN.CENTER run = p.runs[0] if p.runs else p.add_run() run.font.size = Pt(font_size) run.font.bold = True run.font.color.rgb = RGBColor(255, 255, 255) # Data rows for ri, row in enumerate(rows): bg = RGBColor(235, 241, 251) if ri % 2 == 0 else RGBColor(255, 255, 255) for ci, val in enumerate(row): cell = table.cell(ri + 1, ci) cell.text = str(val) cell.fill.solid() cell.fill.fore_color.rgb = bg p = cell.text_frame.paragraphs[0] p.alignment = PP_ALIGN.CENTER run = p.runs[0] if p.runs else p.add_run() run.font.size = Pt(font_size - 1) return table # ------- Slide layout indices ------- # 0 = Title Slide, 1 = Title and Content TITLE_LAYOUT = prs.slide_layouts[0] CONTENT_LAYOUT = prs.slide_layouts[1] BLANK_LAYOUT = prs.slide_layouts[6] # blank # Remove all existing slides (we'll add our own) # Keep the XML structure intact xml_slides = prs.slides._sldIdLst for sl in list(xml_slides): xml_slides.remove(sl) # Track slide rels slide_rels = prs.slides._sldIdLst def new_slide(layout): slide = prs.slides.add_slide(layout) return slide # ============================================================ # SLIDE 1 — TITLE SLIDE # ============================================================ slide = new_slide(TITLE_LAYOUT) clear_slide(slide) # Title box add_textbox(slide, left=0.5, top=1.2, width=9.0, height=2.4, text="EFFECT OF ANTIEPILEPTIC DRUGS ON COGNITIVE FUNCTIONS AND ADVERSE DRUG REACTIONS IN NEWLY DIAGNOSED PAEDIATRIC EPILEPSY", font_size=20, bold=True, align=PP_ALIGN.CENTER, color=(30, 74, 135)) # Subtitle / details add_textbox(slide, left=1.5, top=3.8, width=7.0, height=2.5, text=( "A PROSPECTIVE OBSERVATIONAL STUDY\n\n" "Dhiraj Hospital, S.B.K.S. Medical Institute & Research Centre\n" "Piparia, Vadodara\n\n" "Duration: August 2024 – March 2026\n" "Study Population: 100 Paediatric Patients (Age 6–16 years)" ), font_size=16, bold=False, align=PP_ALIGN.CENTER, color=(60, 60, 60)) # ============================================================ # SLIDE 2 — CONTENTS / OUTLINE # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "CONTENTS") add_bullet_content(slide, [ "1. Introduction & Background", "2. Aim & Objectives", "3. Review of Literature", "4. Materials & Methods", "5. Results", " 5a. Demographic & Baseline Characteristics", " 5b. Clinical Characteristics of Epilepsy", " 5c. Prescription Patterns of AEDs", " 5d. Cognitive Outcomes (ACE-III)", " 5e. Sub-Domain Analysis", " 5f. Safety & ADR Analysis", " 5g. WHO Prescribing Indicators", "6. Discussion", "7. Conclusion", "8. References", ], top=1.0, font_size=16) # ============================================================ # SLIDE 3 — INTRODUCTION # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "INTRODUCTION") add_bullet_content(slide, [ "Epilepsy is a chronic neurological disorder characterized by recurrent unprovoked seizures.", "WHO estimates >50 million people worldwide live with epilepsy; ~80% in low- and middle-income countries.", "Paediatric epilepsy is one of the most common serious neurological disorders in children (incidence: 41–187 per 1,00,000).", "Children with epilepsy face additional challenges including cognitive impairment, behavioural disturbances, and AED-related adverse effects.", "Antiepileptic drugs (AEDs) are the cornerstone of management; ~60–70% achieve seizure control with monotherapy.", "AEDs have variable effects on cognitive function — critical concern in a developing brain.", "Systematic prospective evaluation of cognitive outcomes and ADR safety is scarce, especially in rural Indian settings.", "This study addresses this gap through a 12-month prospective, observational design in a tertiary care rural hospital.", ], top=1.0, font_size=16) # ============================================================ # SLIDE 4 — AIM & OBJECTIVES # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "AIM & OBJECTIVES") # AIM subheading add_textbox(slide, 0.3, 1.05, 9.4, 0.4, "AIM", font_size=18, bold=True, color=(30, 74, 135)) add_textbox(slide, 0.3, 1.45, 9.4, 0.7, "To evaluate the effect of antiepileptic drugs on cognitive functions and to assess the incidence and pattern of adverse drug reactions in newly diagnosed paediatric epilepsy in a tertiary care teaching rural hospital.", font_size=16, color=(40, 40, 40), wrap=True) add_textbox(slide, 0.3, 2.2, 9.4, 0.4, "OBJECTIVES", font_size=18, bold=True, color=(30, 74, 135)) add_bullet_content(slide, [ "1. Assess impact of AED therapy on cognitive functions using ACE-III (baseline vs 12-month follow-up).", "2. Determine incidence of adverse drug reactions (ADRs) associated with AEDs in the paediatric age group.", "3. Evaluate pattern and severity of ADRs during AED therapy.", "4. Compare baseline and follow-up cognitive scores after AED initiation.", "5. Identify AEDs commonly associated with cognitive impairment and ADRs in paediatric population.", ], top=2.6, font_size=16) # ============================================================ # SLIDE 5 — REVIEW OF LITERATURE (key points) # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "REVIEW OF LITERATURE") add_bullet_content(slide, [ "Epilepsy incidence peaks during infancy and early childhood (50–100 per 1,00,000/year); prevalence 3–10/1000 children.", "Cognitive impairment is a major comorbidity — affects attention, memory, executive function, language.", "Seizure-related, disease-related, and AED-related factors all contribute to cognitive dysfunction.", "First-generation AEDs (phenobarbital, phenytoin, carbamazepine) associated with greater cognitive adverse effects.", "Second-generation AEDs (lamotrigine, levetiracetam, oxcarbazepine) generally offer better cognitive profiles.", "Topiramate: significant cognitive side effects; Levetiracetam: behavioural adverse effects (irritability).", "ADR incidence in paediatric epilepsy: 40–60% (higher with polytherapy); dizziness, drowsiness, rash most common.", "ACE-III is a validated multi-domain cognitive tool assessing: Attention, Memory, Fluency, Language, Visuospatial.", "Paucity of prospective, baseline-controlled Indian data on AED cognitive effects in newly diagnosed children.", ], top=1.0, font_size=15) # ============================================================ # SLIDE 6 — MATERIALS & METHODS # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "MATERIALS & METHODS") add_bullet_content(slide, [ "Study Design: Prospective, observational, non-interventional study.", "Setting: Paediatrics Dept., Dhiraj Hospital, S.B.K.S. Medical Institute, Piparia, Vadodara (Tertiary care rural teaching hospital).", "Duration: 18 months — August 2024 to March 2026.", "Sample Size: 100 patients (purposive sampling).", "Age Group: 6–16 years, newly diagnosed epilepsy on AED monotherapy.", "Cognitive Assessment: ACE-III (Addenbrooke's Cognitive Examination-III) at Day 0 (baseline) and 12-month follow-up.", "ACE-III Domains: Attention (18), Memory (26), Fluency (14), Language (26), Visuospatial (16) — Total 100.", "ADR Monitoring: WHO–UMC causality assessment scale; Hartwig severity scale.", "Prescription Analysis: WHO core prescribing indicators (generic name, oral route, NLEM adherence).", "Statistical Methods: Paired Student's t-test (within-group); Independent t-test (between-group).", "Ethics: Approved by Institutional Ethics Committee; informed written consent obtained.", ], top=1.0, font_size=15) # ============================================================ # SLIDE 7 — INCLUSION & EXCLUSION CRITERIA # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "INCLUSION & EXCLUSION CRITERIA") add_textbox(slide, 0.3, 1.05, 4.5, 0.4, "INCLUSION CRITERIA", font_size=17, bold=True, color=(30, 74, 135)) add_bullet_content(slide, [ "Newly diagnosed paediatric epilepsy (age 6–16 years)", "Started on AED monotherapy", "Parents/guardians willing to give informed consent", "Attending OPD/IPD at Dhiraj Hospital", "Hemodynamically stable at baseline", ], left=0.3, top=1.5, width=4.5, height=3.5, font_size=15) add_textbox(slide, 5.1, 1.05, 4.7, 0.4, "EXCLUSION CRITERIA", font_size=17, bold=True, color=(30, 74, 135)) add_bullet_content(slide, [ "Progressive neurological disorder or head injury", "History of alcohol/drug abuse", "Mental retardation or severe psychiatric disorder", "Centrally acting concomitant medications", "Age <6 years or >16 years", "Parents not willing to give informed consent", "Children on AED polytherapy", ], left=5.1, top=1.5, width=4.7, height=3.5, font_size=15) # ============================================================ # SLIDE 8 — RESULTS: DEMOGRAPHICS # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "RESULTS: DEMOGRAPHIC & BASELINE CHARACTERISTICS") # Two-column layout add_textbox(slide, 0.3, 1.05, 9.4, 0.35, "Total Patients: 100 | Completion Rate: 100% | Study Duration: 12 months", font_size=15, bold=True, align=PP_ALIGN.CENTER, color=(30, 74, 135)) # Table: Demographics headers = ["Parameter", "Value (Mean ± SD)", "Range"] rows = [ ["Age (years)", "10.33 ± 3.04", "6 – 16"], ["Weight (kg)", "35.02 ± 12.05", "18.5 – 62.6"], ["Gender (Male / Female)", "58% / 42%", "—"], ["Temperature (°C)", "36.95 ± 0.30", "36.4 – 37.5"], ["Pulse (bpm)", "89.84 ± 11.24", "70 – 110"], ["Systolic BP (mmHg)", "109.91 ± 6.82", "92 – 129"], ["Diastolic BP (mmHg)", "69.23 ± 5.86", "57 – 83"], ["ACE-III Score (Day 0)", "73.50 ± 6.70", "53 – 85"], ["ACE-III Score (12 Months)", "76.79 ± 6.80", "60 – 89"], ["Mean Score Change", "+3.29 ± 3.60", "-7 to +10"], ] add_table_slide(slide, headers, rows, left=0.3, top=1.5, width=9.4, col_widths=[3.5, 3.5, 2.4], font_size=13) # ============================================================ # SLIDE 9 — RESULTS: SEIZURE TYPES # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "RESULTS: CLINICAL CHARACTERISTICS — SEIZURE TYPES") headers = ["Seizure Type", "n (%)", "Baseline Mean", "12-Month Mean", "Mean Change", "Interpretation"] rows = [ ["Focal", "52 (52%)", "73.38", "76.40", "+3.02", "Moderate improvement"], ["Generalised Tonic-Clonic", "27 (27%)", "73.93", "77.26", "+3.33", "Moderate improvement"], ["Absence", "14 (14%)", "70.79", "74.50", "+3.71", "Good improvement"], ["Myoclonic", "7 (7%)", "78.14", "82.43", "+4.29", "Best improvement"], ] add_table_slide(slide, headers, rows, left=0.3, top=1.1, width=9.4, col_widths=[2.4, 1.2, 1.4, 1.4, 1.3, 1.7], font_size=13) add_textbox(slide, 0.3, 4.8, 9.4, 1.8, "Key Findings:\n" "• Focal seizures most common (52%), followed by GTC (27%), Absence (14%), Myoclonic (7%)\n" "• Myoclonic seizure patients showed highest baseline scores AND greatest mean improvement (+4.29)\n" "• Absence seizure patients had lowest baseline ACE-III (70.79) but achieved notable improvement (+3.71)\n" "• All seizure types showed statistically significant cognitive improvement at 12 months", font_size=14, color=(40, 40, 40), wrap=True) # ============================================================ # SLIDE 10 — RESULTS: AED PRESCRIPTION PATTERNS # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "RESULTS: AED PRESCRIPTION PATTERNS") headers = ["AED", "n (%)", "Indication", "Dose (mg/kg/day)", "Frequency"] rows = [ ["Levetiracetam", "27 (27%)", "Focal, GTC, Myoclonic", "10", "BD"], ["Carbamazepine", "23 (23%)", "Focal", "10", "BD"], ["Valproate", "17 (17%)", "GTC, Myoclonic, Absence", "10", "BD"], ["Ethosuximide", "10 (10%)", "Absence", "15", "BD"], ["Lamotrigine", "10 (10%)", "GTC", "0.3", "BD"], ["Oxcarbazepine", "8 (8%)", "Focal", "8", "BD"], ["Phenytoin", "5 (5%)", "GTC", "5", "BD"], ] add_table_slide(slide, headers, rows, left=0.3, top=1.1, width=9.4, col_widths=[2.0, 1.3, 2.6, 1.9, 1.6], font_size=13) add_textbox(slide, 0.3, 5.1, 9.4, 1.3, "Key Points: All 100 patients on MONOTHERAPY | 55% on second-generation AEDs\n" "Levetiracetam most prescribed (27%) | Ethosuximide dominated absence epilepsy (71.4%)\n" "Carbamazepine + Levetiracetam preferred for focal seizures (44.2% + 40.3%)", font_size=14, color=(30, 74, 135), bold=False, wrap=True) # ============================================================ # SLIDE 11 — RESULTS: COGNITIVE OUTCOMES (ACE-III TOTAL) # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "RESULTS: COGNITIVE OUTCOMES — ACE-III TOTAL SCORE BY AED") headers = ["AED", "n", "Baseline Mean", "12-Month Mean", "Mean Change ± SD", "ADR Rate"] rows = [ ["Levetiracetam", "27", "75.70", "78.74", "+3.67 ± 3.48", "22%"], ["Carbamazepine", "23", "72.39", "74.22", "+1.83 ± 3.98", "22%"], ["Valproate", "17", "74.71", "77.47", "+2.76 ± 3.31", "24%"], ["Ethosuximide", "10", "71.70", "76.80", "+5.10 ± 2.13", "30%"], ["Lamotrigine", "10", "72.30", "77.50", "+5.20 ± 2.49", "20%"], ["Oxcarbazepine", "8", "74.75", "79.50", "+4.75 ± 1.58", "25%"], ["Phenytoin", "5", "70.00", "70.00", "0.00 ± 5.57", "40%"], ] add_table_slide(slide, headers, rows, left=0.3, top=1.1, width=9.4, col_widths=[2.0, 0.6, 1.5, 1.5, 2.0, 1.8], font_size=13) add_textbox(slide, 0.3, 5.2, 9.4, 1.2, "Lamotrigine (+5.20) & Ethosuximide (+5.10) — highest cognitive gains\n" "Oxcarbazepine (+4.75) — most consistent benefit (lowest SD: 1.58)\n" "Phenytoin — ZERO net improvement, highest variability (SD 5.57) & highest ADR rate (40%)\n" "77% patients improved | 18% declined | 5% no change", font_size=14, color=(40, 40, 40), wrap=True) # ============================================================ # SLIDE 12 — RESULTS: ACE-III SUB-DOMAIN ANALYSIS # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "RESULTS: ACE-III SUB-DOMAIN ANALYSIS") # Sub-domain table headers = ["Domain", "Max Score", "Day 0 Mean", "12-Month Mean", "Mean Change"] rows = [ ["Attention", "18", "13.90", "14.46", "+0.56"], ["Memory", "26", "19.57", "20.37", "+0.80"], ["Fluency", "14", "9.55", "10.10", "+0.55"], ["Language", "26", "19.91", "20.70", "+0.79"], ["Visuospatial", "16", "10.57", "11.16", "+0.59"], ["TOTAL", "100", "73.50", "76.79", "+3.29"], ] add_table_slide(slide, headers, rows, left=0.3, top=1.1, width=9.4, col_widths=[2.0, 1.5, 1.8, 1.8, 2.3], font_size=14) add_textbox(slide, 0.3, 4.4, 9.4, 0.35, "DOMAIN-SPECIFIC HIGHLIGHTS", font_size=16, bold=True, color=(30, 74, 135)) add_bullet_content(slide, [ "Memory (+0.80) & Language (+0.79) — greatest improvement (seizure control → hippocampal/cortical recovery)", "Verbal Fluency (68.2%) & Visuospatial (66.1%) — most impaired at baseline", "Ethosuximide: best gains in Attention (+1.40) and Memory (+1.50)", "Lamotrigine: best Language improvement (+1.70)", "Phenytoin: declined in Attention (-0.40), Memory (-0.20), and Fluency (-0.40)", ], top=4.8, font_size=14) # ============================================================ # SLIDE 13 — RESULTS: DOMAIN CHANGE BY AED # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "RESULTS: MEAN CHANGE IN ACE-III DOMAINS BY AED") headers = ["AED", "Attention", "Memory", "Fluency", "Language", "Visuospatial", "Total"] rows = [ ["Levetiracetam", "+0.48", "+0.96", "+0.81", "+0.85", "+0.56", "+3.67"], ["Carbamazepine", "+0.35", "+0.48", "+0.17", "+0.35", "+0.48", "+1.83"], ["Valproate", "+0.53", "+0.47", "+0.35", "+0.59", "+0.82", "+2.76"], ["Ethosuximide", "+1.40", "+1.50", "+1.10", "+1.10", "0.00", "+5.10"], ["Lamotrigine", "+0.60", "+1.30", "+0.90", "+1.70", "+0.70", "+5.20"], ["Oxcarbazepine", "+1.00", "+1.00", "+0.63", "+1.25", "+0.88", "+4.75"], ["Phenytoin", "-0.40", "-0.20", "-0.40", "0.00", "+1.00", "0.00"], ] add_table_slide(slide, headers, rows, left=0.3, top=1.1, width=9.4, col_widths=[1.9, 1.2, 1.2, 1.2, 1.2, 1.5, 1.2], font_size=13) add_textbox(slide, 0.3, 5.2, 9.4, 1.1, "Phenytoin consistently shows negative or no change across cognitive domains — attention, memory, and fluency all declined.\n" "Ethosuximide and Lamotrigine demonstrate the most favourable domain-specific profiles.\n" "Oxcarbazepine shows uniformly consistent gains across all domains with low variability.", font_size=14, color=(40, 40, 40), wrap=True) # ============================================================ # SLIDE 14 — RESULTS: SAFETY & ADR ANALYSIS # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "RESULTS: SAFETY & ADVERSE DRUG REACTION ANALYSIS") # ADR type table headers_adr = ["ADR Type", "Count (n=24)", "% of All ADRs", "Causality (WHO-UMC)"] rows_adr = [ ["Dizziness", "7", "29.2%", "Probable"], ["Drowsiness", "6", "25.0%", "Probable"], ["Rash", "6", "25.0%", "Possible"], ["GI Upset", "3", "12.5%", "Possible"], ["Irritability", "2", "8.3%", "Probable"], ] add_table_slide(slide, headers_adr, rows_adr, left=0.3, top=1.1, width=5.0, col_widths=[1.5, 1.2, 1.3, 1.0], font_size=13) # ADR by AED table headers_aed = ["AED", "ADR Rate", "Types"] rows_aed = [ ["Phenytoin", "40%", "Dizziness, Drowsiness"], ["Ethosuximide", "30%", "GI upset, Dizziness"], ["Oxcarbazepine", "25%", "Rash, Dizziness"], ["Valproate", "23.5%", "GI upset, Rash"], ["Levetiracetam", "22.2%", "Drowsiness, Dizziness, Rash, Irritability"], ["Carbamazepine", "21.7%", "Rash, Drowsiness, Dizziness"], ["Lamotrigine", "20%", "Drowsiness, Dizziness"], ] add_table_slide(slide, headers_aed, rows_aed, left=5.4, top=1.1, width=4.3, col_widths=[1.5, 1.0, 1.8], font_size=12) add_textbox(slide, 0.3, 5.45, 9.4, 1.0, "Overall ADR incidence: 24% | All mild-to-moderate | No Serious Adverse Events (SAEs)\n" "15 Probable + 9 Possible (WHO-UMC) | Children 6–9 years: 50% of all ADRs\n" "Lamotrigine: lowest ADR rate (20%) with highest cognitive gains — most favourable profile", font_size=14, color=(30, 74, 135), bold=False, wrap=True) # ============================================================ # SLIDE 15 — RESULTS: WHO PRESCRIBING INDICATORS # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "RESULTS: WHO PRESCRIBING INDICATORS") headers = ["Sr.", "Indicator", "Result", "Standard"] rows = [ ["I", "Average number of AEDs prescribed per patient", "1", "As low as possible"], ["II", "% AEDs prescribed by generic name", "25%", "Promote generics"], ["III", "% AEDs prescribed by oral route", "100%", "Prefer oral route"], ["IV", "% AEDs from NLEM 2022", "72%", "Prefer NLEM drugs"], ] add_table_slide(slide, headers, rows, left=1.0, top=1.3, width=8.0, col_widths=[0.5, 4.0, 1.5, 2.0], font_size=14) add_textbox(slide, 0.3, 4.1, 9.4, 2.5, "INTERPRETATION:\n" "• Average drugs/patient = 1: Excellent — strict monotherapy maintained, minimizing cognitive confounding\n" "• 100% oral prescribing: Optimal adherence to standard care; no parenteral administration\n" "• 72% NLEM adherence: Good — reflects guideline-concordant prescribing practice in resource-limited setting\n" "• 25% generic prescribing: Scope for improvement — higher generic use would reduce treatment costs\n" "• Predominance of second-generation AEDs (55%): Reflects evidence-based, modern prescribing practice", font_size=14, color=(40, 40, 40), wrap=True) # ============================================================ # SLIDE 16 — DISCUSSION (Part 1) # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "DISCUSSION (1/2)") add_bullet_content(slide, [ "Study enrolled 100 treatment-naïve paediatric patients (6–16 years); 100% completion rate — robust dataset.", "Mean ACE-III improved 73.50 → 76.79 (+3.29); 77% improved — consistent with prior literature on seizure control benefits.", "Focal seizures most prevalent (52%) — consistent with global epidemiological data (Domańska et al.; Kaushik et al.).", "Absence seizures: lowest baseline ACE-III (70.79) — aligns with known cognitive profile of childhood absence epilepsy.", "Lamotrigine (+5.20) & Ethosuximide (+5.10): highest cognitive gains — consistent with Rosati et al. and Aldenkamp et al.", "Oxcarbazepine: consistent improvement (+4.75), lowest variability (SD 1.58) — better tolerability vs. carbamazepine.", "Phenytoin: zero net cognitive change, highest ADR rate (40%) — supports declining role in modern paediatric practice.", "Levetiracetam (most prescribed): moderate cognitive benefit (+3.67), notable behavioural ADRs (irritability) — Naeije et al.", "Memory & Language: greatest improvement domains — hippocampal/cortical recovery after seizure suppression.", ], top=1.0, font_size=14) # ============================================================ # SLIDE 17 — DISCUSSION (Part 2) # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "DISCUSSION (2/2)") add_bullet_content(slide, [ "Overall ADR incidence 24% — lower than polytherapy studies (30–35%); Kaushik et al., Bansal et al.", "Dizziness (29.2%) & Drowsiness (25%) most common ADRs — consistent with CNS depressant effects.", "Children aged 6–9 years: 50% of ADRs — pharmacokinetic vulnerability (immature hepatic enzymes, altered Vd).", "All ADRs mild-to-moderate; no SAEs — monotherapy safety advantage confirmed.", "55% second-generation AED use — aligns with global trends; ILAE/NICE NG217 guideline-concordant practice.", "WHO indicators: avg. 1 drug/patient, 100% oral, 72% NLEM — reflects responsible prescribing in rural settings.", "ACE-III: comprehensive multi-domain tool (5 domains); superior to single-domain screening tools (MMSE).", "Strengths: prospective design, baseline cognitive data, 100% follow-up, standardized ADR causality assessment.", "Limitations: single-centre, no control group; small subgroup sizes; socioeconomic factors not formally assessed.", ], top=1.0, font_size=14) # ============================================================ # SLIDE 18 — CONCLUSION # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "CONCLUSION") add_bullet_content(slide, [ "AED monotherapy in newly diagnosed paediatric epilepsy produces STATISTICALLY SIGNIFICANT cognitive improvement over 12 months (73.50 → 76.79, p<0.05).", "77% of patients demonstrated cognitive improvement; effective seizure control facilitates cognitive recovery.", "Lamotrigine & Ethosuximide: most favourable cognitive AND safety outcomes — preferred choices where indicated.", "Oxcarbazepine: consistent improvement with low variability and better tolerability.", "Phenytoin: NO net cognitive benefit, highest ADR rate (40%) — declining role in paediatric epilepsy reinforced.", "Overall ADR incidence 24% — monotherapy safety advantage confirmed; all events mild-to-moderate.", "Children aged 6–9 years: highest ADR vulnerability — requires enhanced monitoring.", "55% second-generation AED use — guideline-concordant practice; NLEM adherence 72%.", "Future need: multicentre studies, control groups, comprehensive neuropsychological assessments, socioeconomic variables.", "Key takeaway: Baseline cognitive assessment + preferential second-generation AEDs + structured pharmacovigilance = optimal paediatric epilepsy management.", ], top=1.0, font_size=14) # ============================================================ # SLIDE 19 — REFERENCES (selected) # ============================================================ slide = new_slide(CONTENT_LAYOUT) clear_slide(slide) add_heading_box(slide, "SELECTED REFERENCES") add_bullet_content(slide, [ "1. Fisher RS et al. ILAE official report: a practical clinical definition of epilepsy. Epilepsia. 2014;55(4):475-82.", "2. WHO. Epilepsy: a public health imperative. Geneva: WHO; 2019.", "3. Rosati A, De Masi S, Guerrini R. AED treatment in children with epilepsy. CNS Drugs. 2015;29(10):847-63.", "4. Glauser T et al. ILAE treatment guidelines: evidence-based analysis of AED efficacy. Epilepsia. 2006;47(7):1094-120.", "5. NICE Guideline NG217. Epilepsies: diagnosis and management. NICE; 2022, updated 2025.", "6. Rai J et al. Effect of AEDs on cognitive functions: a prospective study. IJBCP. 2017;6(4):855.", "7. Kaushik JS et al. Pattern of AED use and ADRs in paediatric epilepsy. Neurology India.", "8. Bansal D et al. Adverse effects of AEDs in North Indian paediatric outpatients. Clin Neuropharmacol. 2013.", "9. Joshi R et al. Adverse effects & drug load of AEDs: monotherapy vs. polytherapy. Indian J Med Res. 2017.", "10. Beishon L et al. Addenbrooke's Cognitive Examination III. Cochrane Systematic Review. 2019.", "11. Takenoshita S et al. Validation of ACE-III for detecting mild cognitive impairment. BMC Geriatrics. 2019.", "12. Scheffer IE et al. ILAE classification of the epilepsies: Position paper. Epilepsia. 2017;58(4):512-21.", ], top=1.0, font_size=13) # ============================================================ # SLIDE 20 — THANK YOU # ============================================================ slide = new_slide(TITLE_LAYOUT) clear_slide(slide) add_textbox(slide, 1.0, 2.0, 8.0, 1.5, "THANK YOU", font_size=44, bold=True, align=PP_ALIGN.CENTER, color=(30, 74, 135)) add_textbox(slide, 1.5, 3.7, 7.0, 2.0, "Dhiraj Hospital, S.B.K.S. Medical Institute & Research Centre\n" "Piparia, Vadodara\n\n" "Prospective Study — Effect of Antiepileptic Drugs on\n" "Cognitive Functions in Paediatric Epilepsy", font_size=16, align=PP_ALIGN.CENTER, color=(80, 80, 80)) # Save prs.save(out) print(f"Saved: {out}") print(f"Total slides: {len(prs.slides)}") PYEOF python3 /tmp/workspace/thesis-ppt/build_thesis_ppt.py

python3 -c " from pptx import Presentation prs = Presentation('/tmp/workspace/thesis-ppt/Thesis_AED_Cognitive_Paediatric.pptx') print(f'Total slides: {len(prs.slides)}') for i, slide in enumerate(prs.slides, 1): texts = [] for shape in slide.shapes: if shape.has_text_frame: t = shape.text_frame.text.strip() if t: texts.append(t[:60]) print(f'Slide {i}: {\" | \".join(texts[:3])}') print('File size:', __import__(\"os\").path.getsize('/tmp/workspace/thesis-ppt/Thesis_AED_Cognitive_Paediatric.pptx'), 'bytes') "

python3 -c " from pptx import Presentation prs = Presentation('/tmp/workspace/thesis-ppt/Thesis_AED_Cognitive_Paediatric.pptx') print(f'Current slides: {len(prs.slides)}') for i, slide in enumerate(prs.slides, 1): texts = [] for shape in slide.shapes: if shape.has_text_frame: t = shape.text_frame.text.strip() if t: texts.append(t[:80]) print(f'Slide {i}: {\" | \".join(texts[:2])}') "

cat > /tmp/workspace/thesis-ppt/build_v2.py << 'PYEOF' """ Thesis PPTX v2 — 14 slides, Dr. Prachi style """ from pptx import Presentation from pptx.util import Inches, Pt from pptx.dml.color import RGBColor from pptx.enum.text import PP_ALIGN src = "/tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363/Dr. Prachi.pptx" out = "/tmp/workspace/thesis-ppt/Thesis_AED_Paediatric_v2.pptx" prs = Presentation(src) # ---- Remove all existing slides ---- xml_slides = prs.slides._sldIdLst for sl in list(xml_slides): xml_slides.remove(sl) TITLE_LAYOUT = prs.slide_layouts[0] CONTENT_LAYOUT = prs.slide_layouts[1] NAVY = (30, 74, 135) WHITE = (255, 255, 255) DARK = (30, 30, 30) LIGHT = (235, 241, 251) TEAL = (0, 112, 130) # ---- Helpers ---- def ns(slide, layout): return prs.slides.add_slide(layout) def clear(slide): sp_tree = slide.shapes._spTree for sp in list(sp_tree): tag = sp.tag.split('}')[-1] if '}' in sp.tag else sp.tag if tag in ('grpSpPr',): continue sp_tree.remove(sp) def hbox(slide, title, left=0.25, top=0.18, width=9.5, height=0.60, size=24): box = slide.shapes.add_shape(1, Inches(left), Inches(top), Inches(width), Inches(height)) box.fill.solid(); box.fill.fore_color.rgb = RGBColor(*NAVY) box.line.fill.background() tf = box.text_frame; tf.word_wrap = False p = tf.paragraphs[0]; p.alignment = PP_ALIGN.CENTER r = p.add_run(); r.text = title r.font.size = Pt(size); r.font.bold = True r.font.color.rgb = RGBColor(*WHITE) return box def tb(slide, text, left, top, width, height, size=15, bold=False, color=DARK, align=PP_ALIGN.LEFT, wrap=True, italic=False): b = slide.shapes.add_textbox(Inches(left), Inches(top), Inches(width), Inches(height)) tf = b.text_frame; tf.word_wrap = wrap p = tf.paragraphs[0]; p.alignment = align r = p.add_run(); r.text = text r.font.size = Pt(size); r.font.bold = bold; r.font.italic = italic r.font.color.rgb = RGBColor(*color) return b def bullets(slide, items, left, top, width, height, size=15, gap=False): b = slide.shapes.add_textbox(Inches(left), Inches(top), Inches(width), Inches(height)) tf = b.text_frame; tf.word_wrap = True first = True for item in items: if isinstance(item, dict): # section heading p = tf.paragraphs[0] if first else tf.add_paragraph() first = False p.alignment = PP_ALIGN.LEFT r = p.add_run(); r.text = item['h'] r.font.size = Pt(size + 1); r.font.bold = True r.font.color.rgb = RGBColor(*NAVY) if gap: ep = tf.add_paragraph(); ep.add_run().text = "" else: lvl = 0 if item.startswith(' '): lvl = 1; item = item.strip() p = tf.paragraphs[0] if first else tf.add_paragraph() first = False p.level = lvl p.alignment = PP_ALIGN.JUSTIFY r = p.add_run(); r.text = item r.font.size = Pt(size - lvl) r.font.color.rgb = RGBColor(*DARK) return b def table(slide, headers, rows, left, top, width, col_widths, size=12, row_h=0.40): nr = len(rows) + 1; nc = len(headers) t = slide.shapes.add_table( nr, nc, Inches(left), Inches(top), Inches(width), Inches(nr * row_h + 0.08) ).table for i, w in enumerate(col_widths): t.columns[i].width = Inches(w) for ci, h in enumerate(headers): cell = t.cell(0, ci) cell.text = h cell.fill.solid(); cell.fill.fore_color.rgb = RGBColor(*NAVY) p = cell.text_frame.paragraphs[0]; p.alignment = PP_ALIGN.CENTER r = p.runs[0] if p.runs else p.add_run() r.font.size = Pt(size); r.font.bold = True; r.font.color.rgb = RGBColor(*WHITE) for ri, row in enumerate(rows): bg = RGBColor(*LIGHT) if ri % 2 == 0 else RGBColor(*WHITE) for ci, val in enumerate(row): cell = t.cell(ri+1, ci) cell.text = str(val) cell.fill.solid(); cell.fill.fore_color.rgb = bg p = cell.text_frame.paragraphs[0]; p.alignment = PP_ALIGN.CENTER r = p.runs[0] if p.runs else p.add_run() r.font.size = Pt(size - 1) return t def divider(slide, left, top, width, color=NAVY): line = slide.shapes.add_shape(1, Inches(left), Inches(top), Inches(width), Inches(0.04)) line.fill.solid(); line.fill.fore_color.rgb = RGBColor(*color) line.line.fill.background() # ================================================================ # SLIDE 1 — TITLE # ================================================================ sl = ns(None, TITLE_LAYOUT); clear(sl) tb(sl, "EFFECT OF ANTIEPILEPTIC DRUGS ON COGNITIVE FUNCTIONS\nAND ADVERSE DRUG REACTIONS IN\nNEWLY DIAGNOSED PAEDIATRIC EPILEPSY", left=0.5, top=1.1, width=9.0, height=2.0, size=22, bold=True, color=NAVY, align=PP_ALIGN.CENTER) divider(sl, 1.5, 3.2, 7.0) tb(sl, "A PROSPECTIVE OBSERVATIONAL STUDY | August 2024 – March 2026", left=0.5, top=3.35, width=9.0, height=0.45, size=15, bold=True, color=TEAL, align=PP_ALIGN.CENTER) tb(sl, "Dhiraj Hospital, S.B.K.S. Medical Institute & Research Centre, Piparia, Vadodara\n" "Paediatrics Department | 100 Patients (Age 6–16 years) | AED Monotherapy", left=1.0, top=3.9, width=8.0, height=1.0, size=15, color=(60,60,60), align=PP_ALIGN.CENTER) # ================================================================ # SLIDE 2 — CONTENTS # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "CONTENTS") bullets(sl, [ "1. Introduction & Background", "2. Aim & Objectives", "3. Review of Literature", "4. Materials & Methods (Study Design, Inclusion/Exclusion, Statistical Methods)", "5. Results", " 5a. Demographic & Baseline Characteristics", " 5b. Clinical Characteristics of Epilepsy (Seizure Types)", " 5c. AED Prescription Patterns", " 5d. Cognitive Outcomes — ACE-III Total Score by AED", " 5e. ACE-III Sub-Domain Analysis & Domain-Specific Changes", " 5f. Safety & ADR Analysis", " 5g. WHO Prescribing Indicators", "6. Discussion", "7. Conclusion & Recommendations", "8. References", ], left=0.5, top=0.9, width=9.0, height=5.8, size=15) # ================================================================ # SLIDE 3 — INTRODUCTION + REVIEW OF LITERATURE # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "INTRODUCTION & REVIEW OF LITERATURE") bullets(sl, [ {"h": "Background"}, "Epilepsy: chronic neurological disorder with recurrent unprovoked seizures; >50 million worldwide (WHO).", "Paediatric epilepsy incidence: 41–187/1,00,000 children; prevalence 3–10/1000 (higher in LMICs).", "Developing brain is uniquely vulnerable — seizures disrupt synaptogenesis, myelination, and network maturation.", "~60–70% achieve seizure control with first-line AED monotherapy (Kwan & Brodie, 2000).", {"h": "Cognitive Impact & AED Effects"}, "Cognitive impairment (attention, memory, fluency, language, visuospatial) is a major comorbidity.", "First-gen AEDs (phenobarbital, phenytoin, carbamazepine) — greater cognitive adverse effects.", "Second-gen AEDs (lamotrigine, levetiracetam, oxcarbazepine) — generally favourable cognitive profiles.", "Topiramate: significant cognitive side effects; Levetiracetam: behavioural adverse effects.", "ADR incidence in paediatric epilepsy: 40–60% (higher with polytherapy); dizziness, drowsiness, rash most common.", {"h": "Research Gap"}, "Paucity of prospective, baseline-controlled Indian data from rural/resource-limited settings — justifies this study.", ], left=0.3, top=0.9, width=9.4, height=5.9, size=13, gap=True) # ================================================================ # SLIDE 4 — AIM & OBJECTIVES # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "AIM & OBJECTIVES") tb(sl, "AIM", left=0.3, top=0.9, width=9.4, height=0.35, size=17, bold=True, color=NAVY) tb(sl, "To evaluate the effect of antiepileptic drugs on cognitive functions and to assess the incidence and " "pattern of adverse drug reactions in newly diagnosed cases of epilepsy in the paediatric age group " "attending a tertiary care teaching rural hospital.", left=0.3, top=1.28, width=9.4, height=0.75, size=14, color=DARK, wrap=True) divider(sl, 0.3, 2.08, 9.4) tb(sl, "OBJECTIVES", left=0.3, top=2.16, width=9.4, height=0.35, size=17, bold=True, color=NAVY) bullets(sl, [ "1. Assess impact of AED therapy on cognitive functions in children using ACE-III (Day 0 vs. 12-month follow-up).", "2. Determine incidence of adverse drug reactions (ADRs) associated with AEDs in the paediatric age group.", "3. Evaluate pattern and severity of ADRs occurring during AED therapy.", "4. Compare baseline and follow-up cognitive function scores after initiation of AED treatment.", "5. Identify AEDs commonly associated with cognitive impairment and ADRs in the paediatric population.", ], left=0.3, top=2.55, width=9.4, height=3.7, size=15) # ================================================================ # SLIDE 5 — MATERIALS & METHODS (includes I/E criteria) # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "MATERIALS & METHODS") # Left column bullets(sl, [ {"h": "Study Design & Setting"}, "Prospective, observational, non-interventional study.", "Paediatrics Dept., Dhiraj Hospital, SBKS MIR Centre, Piparia, Vadodara.", "Duration: 18 months (Aug 2024 – Mar 2026).", "Sample: 100 patients, purposive sampling.", {"h": "Cognitive Assessment"}, "ACE-III at Day 0 (baseline) & 12-month follow-up.", "5 domains: Attention (18), Memory (26), Fluency (14), Language (26), Visuospatial (16) — Total/100.", {"h": "ADR Monitoring & Statistics"}, "WHO–UMC causality scale; Hartwig severity scale.", "Paired t-test (within-group); Independent t-test (between-group).", "Ethics approval; written informed consent obtained.", ], left=0.3, top=0.9, width=4.6, height=5.8, size=13, gap=True) # Right column — Inclusion / Exclusion tb(sl, "INCLUSION CRITERIA", left=5.2, top=0.9, width=4.5, height=0.35, size=14, bold=True, color=NAVY) bullets(sl, [ "Newly diagnosed epilepsy, age 6–16 years", "Started on AED monotherapy", "Willing to give informed consent", "Attending OPD/IPD Dhiraj Hospital", "Hemodynamically stable at baseline", ], left=5.2, top=1.28, width=4.5, height=1.9, size=13) divider(sl, 5.2, 3.22, 4.5) tb(sl, "EXCLUSION CRITERIA", left=5.2, top=3.3, width=4.5, height=0.35, size=14, bold=True, color=NAVY) bullets(sl, [ "Progressive neurological disorder / head injury", "History of alcohol/drug abuse", "Mental retardation / severe psychiatric disorder", "Concomitant centrally acting medications", "Age <6 years or >16 years", "Parents not willing to consent", "Prior AED use / polytherapy", ], left=5.2, top=3.68, width=4.5, height=2.6, size=13) # ================================================================ # SLIDE 6 — RESULTS: DEMOGRAPHICS & BASELINE # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "RESULTS: DEMOGRAPHIC & BASELINE CHARACTERISTICS") tb(sl, "n = 100 | 100% Completion Rate | Male 58% / Female 42% | Mean Age: 10.33 ± 3.04 years", left=0.3, top=0.9, width=9.4, height=0.35, size=14, bold=True, color=TEAL, align=PP_ALIGN.CENTER) table(sl, ["Parameter", "Mean ± SD", "Range"], [ ["Age (years)", "10.33 ± 3.04", "6 – 16"], ["Weight (kg)", "35.02 ± 12.05", "18.5 – 62.6"], ["Temperature (°C)", "36.95 ± 0.30", "36.4 – 37.5"], ["Pulse (bpm)", "89.84 ± 11.24", "70 – 110"], ["Systolic BP (mmHg)", "109.91 ± 6.82", "92 – 129"], ["Diastolic BP (mmHg)", "69.23 ± 5.86", "57 – 83"], ["RR (/min)", "20.43 ± 3.27", "13 – 29"], ["ACE-III Score (Day 0)","73.50 ± 6.70", "53 – 85"], ["ACE-III Score (12 Mo)","76.79 ± 6.80", "60 – 89"], ["Mean Score Change", "+3.29 ± 3.60", "-7 to +10"], ], left=0.3, top=1.32, width=9.4, col_widths=[3.8, 3.0, 2.6], size=13, row_h=0.41) tb(sl, "77% patients improved | 18% declined | 5% no change | Statistical significance: p < 0.05", left=0.3, top=6.6, width=9.4, height=0.35, size=13, bold=True, color=NAVY, align=PP_ALIGN.CENTER) # ================================================================ # SLIDE 7 — RESULTS: SEIZURE TYPES + AED PRESCRIPTION # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "RESULTS: SEIZURE TYPES & AED PRESCRIPTION PATTERNS") tb(sl, "SEIZURE TYPE DISTRIBUTION & COGNITIVE CHANGE", left=0.3, top=0.9, width=4.6, height=0.32, size=13, bold=True, color=NAVY) table(sl, ["Seizure Type", "n (%)", "Δ ACE-III"], [ ["Focal", "52 (52%)", "+3.02"], ["GTC", "27 (27%)", "+3.33"], ["Absence", "14 (14%)", "+3.71"], ["Myoclonic", "7 (7%)", "+4.29"], ], left=0.3, top=1.25, width=4.6, col_widths=[2.3, 1.2, 1.1], size=12, row_h=0.42) tb(sl, "AED PRESCRIPTION FREQUENCY", left=5.1, top=0.9, width=4.6, height=0.32, size=13, bold=True, color=NAVY) table(sl, ["AED", "n (%)", "Seizure Type"], [ ["Levetiracetam", "27 (27%)", "Focal, GTC, Myoclonic"], ["Carbamazepine", "23 (23%)", "Focal"], ["Valproate", "17 (17%)", "GTC, Myoclonic, Absence"], ["Ethosuximide", "10 (10%)", "Absence"], ["Lamotrigine", "10 (10%)", "GTC"], ["Oxcarbazepine", "8 (8%)", "Focal"], ["Phenytoin", "5 (5%)", "GTC"], ], left=5.1, top=1.25, width=4.6, col_widths=[1.7, 1.0, 1.9], size=12, row_h=0.42) divider(sl, 0.3, 4.5, 9.4) bullets(sl, [ "All 100 patients on MONOTHERAPY — eliminates polypharmacy confounding.", "55% on second-generation AEDs (levetiracetam, lamotrigine, oxcarbazepine) — reflects modern, guideline-concordant practice.", "Ethosuximide dominated absence epilepsy (71.4%); Carbamazepine + Levetiracetam preferred for focal seizures (44.2% + 40.3%).", "AED selection significantly correlated with seizure type (p < 0.05).", ], left=0.3, top=4.6, width=9.4, height=2.0, size=13) # ================================================================ # SLIDE 8 — RESULTS: COGNITIVE OUTCOMES — ACE-III BY AED # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "RESULTS: COGNITIVE OUTCOMES — ACE-III TOTAL SCORE BY AED") table(sl, ["AED", "n", "Baseline\nMean", "12-Month\nMean", "Mean Change\n± SD", "ADR\nRate"], [ ["Levetiracetam", "27", "75.70", "78.74", "+3.67 ± 3.48", "22%"], ["Carbamazepine", "23", "72.39", "74.22", "+1.83 ± 3.98", "22%"], ["Valproate", "17", "74.71", "77.47", "+2.76 ± 3.31", "24%"], ["Ethosuximide", "10", "71.70", "76.80", "+5.10 ± 2.13", "30%"], ["Lamotrigine", "10", "72.30", "77.50", "+5.20 ± 2.49", "20%"], ["Oxcarbazepine", "8", "74.75", "79.50", "+4.75 ± 1.58", "25%"], ["Phenytoin", "5", "70.00", "70.00", "0.00 ± 5.57", "40%"], ], left=0.3, top=0.95, width=9.4, col_widths=[1.85, 0.55, 1.4, 1.4, 2.0, 1.0], size=12, row_h=0.45) divider(sl, 0.3, 4.55, 9.4) bullets(sl, [ "Lamotrigine (+5.20) & Ethosuximide (+5.10) — highest cognitive gains with low ADR rates → most favourable profile.", "Oxcarbazepine (+4.75, SD 1.58) — most consistent improvement (lowest variability).", "Carbamazepine (+1.83) — least cognitive benefit among all AEDs studied.", "Phenytoin — ZERO net cognitive improvement; highest variability (SD 5.57) AND highest ADR rate (40%) → declining role in paediatrics.", ], left=0.3, top=4.65, width=9.4, height=2.1, size=13) # ================================================================ # SLIDE 9 — RESULTS: ACE-III SUB-DOMAIN ANALYSIS + DOMAIN BY AED # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "RESULTS: ACE-III SUB-DOMAIN ANALYSIS") tb(sl, "OVERALL SUB-DOMAIN SCORES", left=0.3, top=0.9, width=4.6, height=0.30, size=13, bold=True, color=NAVY) table(sl, ["Domain", "Max", "Day 0", "12 Mo", "Δ"], [ ["Attention", "18", "13.90", "14.46", "+0.56"], ["Memory", "26", "19.57", "20.37", "+0.80"], ["Fluency", "14", "9.55", "10.10", "+0.55"], ["Language", "26", "19.91", "20.70", "+0.79"], ["Visuospatial", "16", "10.57", "11.16", "+0.59"], ["TOTAL", "100","73.50", "76.79", "+3.29"], ], left=0.3, top=1.24, width=4.6, col_widths=[1.5, 0.55, 0.75, 0.75, 1.05], size=12, row_h=0.42) tb(sl, "DOMAIN CHANGE BY AED (Δ)", left=5.1, top=0.9, width=4.6, height=0.30, size=13, bold=True, color=NAVY) table(sl, ["AED", "Att.", "Mem.", "Flu.", "Lang.", "Vis."], [ ["Levetiracetam","+0.48","+0.96","+0.81","+0.85","+0.56"], ["Carbamazepine","+0.35","+0.48","+0.17","+0.35","+0.48"], ["Valproate", "+0.53","+0.47","+0.35","+0.59","+0.82"], ["Ethosuximide", "+1.40","+1.50","+1.10","+1.10","0.00"], ["Lamotrigine", "+0.60","+1.30","+0.90","+1.70","+0.70"], ["Oxcarbazepine","+1.00","+1.00","+0.63","+1.25","+0.88"], ["Phenytoin", "-0.40","-0.20","-0.40","0.00", "+1.00"], ], left=5.1, top=1.24, width=4.6, col_widths=[1.55, 0.6, 0.6, 0.6, 0.6, 0.65], size=11, row_h=0.42) divider(sl, 0.3, 4.45, 9.4) bullets(sl, [ "Memory (+0.80) & Language (+0.79) — greatest gains; reflect hippocampal/cortical recovery after seizure suppression.", "Verbal Fluency (68.2%) & Visuospatial (66.1%) — most impaired at baseline; suggest need for adjunctive educational support.", "Ethosuximide: best Attention (+1.40) & Memory (+1.50). Lamotrigine: best Language (+1.70). Phenytoin: negative change in Attention, Memory, Fluency.", "Age-group analysis: 6–9 yrs — widest variability (SD 4.52); 13–16 yrs — highest improvement (+3.26).", ], left=0.3, top=4.55, width=9.4, height=2.1, size=13) # ================================================================ # SLIDE 10 — RESULTS: SAFETY & ADR # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "RESULTS: SAFETY & ADVERSE DRUG REACTION ANALYSIS") tb(sl, "ADR TYPE DISTRIBUTION (n=24 patients, 24%)", left=0.3, top=0.9, width=5.0, height=0.30, size=13, bold=True, color=NAVY) table(sl, ["ADR Type", "Count", "% of ADRs", "Causality"], [ ["Dizziness", "7", "29.2%", "Probable"], ["Drowsiness", "6", "25.0%", "Probable"], ["Rash", "6", "25.0%", "Possible"], ["GI Upset", "3", "12.5%", "Possible"], ["Irritability","2", "8.3%", "Probable"], ], left=0.3, top=1.24, width=5.0, col_widths=[1.4, 0.8, 1.1, 1.7], size=12, row_h=0.42) tb(sl, "ADR RATE BY AED", left=5.5, top=0.9, width=4.2, height=0.30, size=13, bold=True, color=NAVY) table(sl, ["AED", "ADR Rate", "Main ADRs"], [ ["Phenytoin", "40%", "Dizziness, Drowsiness"], ["Ethosuximide", "30%", "GI upset, Dizziness"], ["Oxcarbazepine", "25%", "Rash, Dizziness"], ["Valproate", "23.5%", "GI upset, Rash"], ["Levetiracetam", "22.2%", "Drowsiness, Rash, Irritability"], ["Carbamazepine", "21.7%", "Rash, Drowsiness"], ["Lamotrigine", "20%", "Drowsiness, Dizziness"], ], left=5.5, top=1.24, width=4.2, col_widths=[1.4, 0.9, 1.9], size=12, row_h=0.42) divider(sl, 0.3, 4.45, 9.4) bullets(sl, [ "Overall ADR incidence: 24% — lower than polytherapy-based studies (30–35%); confirms monotherapy safety advantage.", "All ADRs mild-to-moderate; NO Serious Adverse Events (SAEs); 15 Probable + 9 Possible by WHO-UMC scale.", "Children aged 6–9 years: accounted for 50% of all ADRs — pharmacokinetic vulnerability (immature hepatic enzyme systems).", "Lamotrigine: lowest ADR rate (20%) with highest cognitive gains — most favourable overall profile.", ], left=0.3, top=4.55, width=9.4, height=2.0, size=13) # ================================================================ # SLIDE 11 — RESULTS: WHO PRESCRIBING INDICATORS # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "RESULTS: WHO PRESCRIBING INDICATORS") table(sl, ["No.", "WHO Indicator", "Observed Result", "Standard / Ideal"], [ ["I", "Average no. of AEDs prescribed per patient", "1.0", "As low as possible"], ["II", "% AEDs prescribed by generic name", "25%", "Promote generic use"], ["III","% AEDs prescribed by oral route", "100%", "Prefer oral route"], ["IV", "% AEDs from NLEM 2022", "72%", "Prefer NLEM drugs"], ], left=0.5, top=1.0, width=9.0, col_widths=[0.45, 3.9, 1.9, 2.75], size=13, row_h=0.50) divider(sl, 0.3, 3.2, 9.4) tb(sl, "INTERPRETATION", left=0.3, top=3.3, width=9.4, height=0.30, size=15, bold=True, color=NAVY) bullets(sl, [ "Average 1 drug/patient: Excellent — strict monotherapy throughout, eliminating polypharmacy confounding on cognition.", "100% oral prescribing: Optimal adherence; improves patient convenience and compliance.", "72% NLEM adherence: Good — reflects evidence-based, cost-effective prescribing in resource-limited rural setting.", "25% generic prescribing: Scope for improvement — higher generic use would significantly reduce treatment cost burden.", "55% second-generation AED use: Reflects modern guideline-concordant practice aligned with ILAE/NICE NG217 recommendations.", ], left=0.3, top=3.65, width=9.4, height=2.9, size=14) # ================================================================ # SLIDE 12 — DISCUSSION # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "DISCUSSION") bullets(sl, [ {"h": "Cognitive Outcomes"}, "Mean ACE-III improved 73.50 → 76.79 (+3.29, p<0.05); 77% improved — consistent with literature on seizure control benefits.", "Memory & Language showed greatest gains — hippocampal/cortical recovery after suppression of epileptiform activity.", "Absence seizures: lowest baseline ACE-III (70.79), aligning with recognised cognitive profile of childhood absence epilepsy.", "18% cognitive decline observed — likely reflects refractory epilepsy, structural pathology, or individual AED susceptibility.", {"h": "AED-Specific Findings"}, "Lamotrigine (+5.20) & Ethosuximide (+5.10): best cognitive and safety outcomes — consistent with Rosati et al., Aldenkamp et al.", "Oxcarbazepine (+4.75, SD 1.58): consistent benefit with low variability; better tolerability than carbamazepine (Besag et al.).", "Levetiracetam (27%; +3.67): notable behavioural ADRs (irritability) confirmed by Naeije et al. systematic review.", "Phenytoin: zero net cognitive benefit + highest ADR (40%) → supports declining paediatric use (Chen et al., 2022).", {"h": "Safety & Prescribing"}, "ADR incidence 24% < polytherapy studies (30–35%); children 6–9 yrs: 50% of ADRs — pharmacokinetic vulnerability.", "WHO indicators: avg 1 drug/patient, 100% oral, 72% NLEM — responsible prescribing in a rural Indian setting.", "Strengths: prospective design, treatment-naïve patients, 100% follow-up, standardized causality assessment.", "Limitations: single-centre; no control group; small subgroup sizes; socioeconomic factors not formally assessed.", ], left=0.3, top=0.9, width=9.4, height=5.9, size=12.5, gap=True) # ================================================================ # SLIDE 13 — CONCLUSION # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "CONCLUSION") bullets(sl, [ {"h": "Primary Finding"}, "AED monotherapy in newly diagnosed paediatric epilepsy is associated with statistically significant cognitive improvement over 12 months (73.50 → 76.79, p<0.05).", {"h": "AED Performance"}, "Lamotrigine & Ethosuximide: highest cognitive gains (+5.20 / +5.10) with low ADR rates → most favourable overall profile.", "Oxcarbazepine: consistent improvement (+4.75), lowest variability — reliable second-line option.", "Levetiracetam (most prescribed): moderate cognitive benefit (+3.67) but notable behavioural adverse effects.", "Phenytoin: NO net cognitive benefit, highest ADR rate (40%) → declining role in modern paediatric epilepsy confirmed.", {"h": "Safety"}, "Overall ADR incidence: 24% — all mild-to-moderate, no SAEs; monotherapy safety advantage confirmed.", "Children 6–9 years: highest ADR susceptibility — requires age-specific enhanced pharmacovigilance.", {"h": "Guideline Adherence & Future Directions"}, "55% second-generation AED use; 72% NLEM adherence — guideline-concordant practice in a rural setting.", "Key recommendations: baseline cognitive assessment, preferential second-generation AEDs, structured pharmacovigilance.", "Future need: multicentre RCTs with larger cohorts, control groups, comprehensive neuropsychological batteries, and socioeconomic variables.", ], left=0.3, top=0.9, width=9.4, height=5.9, size=13, gap=True) # ================================================================ # SLIDE 14 — REFERENCES + THANK YOU # ================================================================ sl = ns(None, CONTENT_LAYOUT); clear(sl) hbox(sl, "SELECTED REFERENCES") bullets(sl, [ "1. Fisher RS et al. ILAE official report: a practical clinical definition of epilepsy. Epilepsia. 2014;55(4):475-82.", "2. WHO. Epilepsy: a public health imperative. Geneva: WHO; 2019.", "3. Glauser T et al. ILAE treatment guidelines: evidence-based analysis. Epilepsia. 2006;47(7):1094-120.", "4. NICE Guideline NG217. Epilepsies: diagnosis and management. NICE; 2022 (updated 2025).", "5. Rosati A, De Masi S, Guerrini R. AED treatment in children. CNS Drugs. 2015;29(10):847-63.", "6. Rai J et al. Effect of AEDs on cognitive functions: a prospective study. IJBCP. 2017;6(4):855.", "7. Kaushik JS et al. Pattern of AED use and ADRs in paediatric epilepsy. Neurology India.", "8. Bansal D et al. Adverse effects of AEDs in North Indian paediatric outpatients. Clin Neuropharmacol. 2013.", "9. Joshi R et al. Adverse effects & drug load: monotherapy vs. polytherapy. Indian J Med Res. 2017.", "10. Beishon L et al. Addenbrooke's Cognitive Examination III (Cochrane Review). 2019.", "11. Scheffer IE et al. ILAE classification of the epilepsies. Epilepsia. 2017;58(4):512-21.", "12. Takenoshita S et al. Validation of ACE-III for detecting MCI in Japan. BMC Geriatrics. 2019.", ], left=0.3, top=0.9, width=9.4, height=4.5, size=13) divider(sl, 0.3, 5.55, 9.4) tb(sl, "THANK YOU", left=0.3, top=5.7, width=9.4, height=0.6, size=30, bold=True, color=NAVY, align=PP_ALIGN.CENTER) tb(sl, "Dhiraj Hospital, S.B.K.S. Medical Institute & Research Centre, Piparia, Vadodara", left=0.3, top=6.35, width=9.4, height=0.40, size=13, color=(80,80,80), align=PP_ALIGN.CENTER) # ---- Save ---- prs.save(out) print(f"Saved: {out}") print(f"Total slides: {len(prs.slides)}") PYEOF python3 /tmp/workspace/thesis-ppt/build_v2.py

python3 -c " from pptx import Presentation import os prs = Presentation('/tmp/workspace/thesis-ppt/Thesis_AED_Paediatric_v2.pptx') print(f'Total slides: {len(prs.slides)}') print(f'File size: {os.path.getsize(\"/tmp/workspace/thesis-ppt/Thesis_AED_Paediatric_v2.pptx\")} bytes') for i, slide in enumerate(prs.slides, 1): texts = [s.text_frame.text.strip()[:70] for s in slide.shapes if s.has_text_frame and s.text_frame.text.strip()] print(f'Slide {i}: {\" | \".join(texts[:2])}') "

cd /tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363 && python3 -c " from pptx import Presentation from pptx.util import Emu import lxml.etree as etree prs = Presentation('Dr. Prachi.pptx') print(f'Slide size: {prs.slide_width.inches} x {prs.slide_height.inches}') print(f'Total slides: {len(prs.slides)}') # Dump full XML of slides 1-4 to understand exact structure for i in [0, 1, 2, 3]: slide = prs.slides[i] print(f'\n\n========== SLIDE {i+1} (Layout: {slide.slide_layout.name}) ==========') xml = etree.tostring(slide._element, pretty_print=True).decode() print(xml[:6000]) " 2>&1 | head -400

cd /tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363 && python3 -c " from pptx import Presentation import lxml.etree as etree prs = Presentation('Dr. Prachi.pptx') # Check the slide master background and theme colors sm = prs.slide_master bg_xml = etree.tostring(sm.background._element, pretty_print=True).decode() print('MASTER BACKGROUND:') print(bg_xml[:2000]) # Get theme theme_el = sm._element.find('.//{http://schemas.openxmlformats.org/drawingml/2006/main}theme') if theme_el is not None: print('\nTHEME COLORS:') fmts = theme_el.findall('.//{http://schemas.openxmlformats.org/drawingml/2006/main}dk1') + \ theme_el.findall('.//{http://schemas.openxmlformats.org/drawingml/2006/main}lt1') + \ theme_el.findall('.//{http://schemas.openxmlformats.org/drawingml/2006/main}dk2') + \ theme_el.findall('.//{http://schemas.openxmlformats.org/drawingml/2006/main}lt2') + \ theme_el.findall('.//{http://schemas.openxmlformats.org/drawingml/2006/main}accent1') for f in fmts[:10]: print(etree.tostring(f, pretty_print=True).decode()[:200]) # Check slides 11 (conclusion) and 12 (recs) for more layout types for i in [10, 11, 12]: slide = prs.slides[i] print(f'\n=== SLIDE {i+1} XML ===') xml = etree.tostring(slide._element, pretty_print=True).decode() print(xml[:3000]) " 2>&1 | head -300

cat > /tmp/workspace/thesis-ppt/build_v3.py << 'PYEOF' """ Thesis PPTX v3 - Uses Dr. Prachi file as the ACTUAL template (copy it, then rebuild slides) - All content 100% from Thesis Final PDF - Exact same visual style: Times New Roman, title underlined+bold, justified body - 14 slides """ import shutil, os, copy from pptx import Presentation from pptx.util import Inches, Pt, Emu from pptx.dml.color import RGBColor from pptx.enum.text import PP_ALIGN from pptx.oxml.ns import qn, nsmap import lxml.etree as etree SRC = "/tmp/workspace/attachments/97b3f354-a7bc-41ee-b251-97ca35ec5363/Dr. Prachi.pptx" OUT = "/tmp/workspace/thesis-ppt/Thesis_AED_Paediatric_FINAL.pptx" # Start from the Dr. Prachi file — we keep its theme, background, slide master shutil.copy2(SRC, OUT) prs = Presentation(OUT) # Remove all existing slides while keeping the slide master/layouts xml_slides = prs.slides._sldIdLst for sl in list(xml_slides): xml_slides.remove(sl) # Layout references from the master (same as Dr. Prachi uses) TITLE_LAYOUT = prs.slide_layouts[0] # Title Slide (used for slide 1) CONTENT_LAYOUT = prs.slide_layouts[1] # Title and Content TNR = "Times New Roman" # ============================================================ # XML builder helpers — exact same approach as Dr. Prachi # ============================================================ A = "http://schemas.openxmlformats.org/drawingml/2006/main" P = "http://schemas.openxmlformats.org/presentationml/2006/main" def emu(inches): return int(inches * 914400) def make_run(text, sz=2000, bold=False, underline=False, italic=False, color_rgb=None, lang="en-IN"): rPr = etree.SubElement(etree.Element('dummy'), f'{{{A}}}rPr') rPr.set('lang', lang) rPr.set('sz', str(sz)) rPr.set('dirty', '0') if bold: rPr.set('b', '1') if underline: rPr.set('u', 'sng') if italic: rPr.set('i', '1') if color_rgb: sf = etree.SubElement(rPr, f'{{{A}}}solidFill') clr = etree.SubElement(sf, f'{{{A}}}srgbClr') clr.set('val', color_rgb) lat = etree.SubElement(rPr, f'{{{A}}}latin') lat.set('typeface', TNR); lat.set('pitchFamily', '18'); lat.set('charset', '0') cs = etree.SubElement(rPr, f'{{{A}}}cs') cs.set('typeface', TNR); cs.set('pitchFamily', '18'); cs.set('charset', '0') t_el = etree.Element(f'{{{A}}}t') t_el.text = text return rPr, t_el def para(text_runs, align='just', indent=False, bullet=None, sz=2000, spacing_pct=None): """Build a <a:p> element. text_runs = list of (text, bold, underline, italic, color)""" p_el = etree.Element(f'{{{A}}}p') pPr = etree.SubElement(p_el, f'{{{A}}}pPr') if indent: pPr.set('marL', '0') pPr.set('indent', '0') if align == 'just': pPr.set('algn', 'just') elif align == 'center': pPr.set('algn', 'ctr') elif align == 'right': pPr.set('algn', 'r') if bullet is False: buNone = etree.SubElement(pPr, f'{{{A}}}buNone') if spacing_pct: lnSpc = etree.SubElement(pPr, f'{{{A}}}lnSpc') spcPct = etree.SubElement(lnSpc, f'{{{A}}}spcPct') spcPct.set('val', str(spacing_pct)) for item in text_runs: if isinstance(item, str): text, bold, ul, ital, color = item, False, False, False, None else: text = item[0] bold = item[1] if len(item) > 1 else False ul = item[2] if len(item) > 2 else False ital = item[3] if len(item) > 3 else False color= item[4] if len(item) > 4 else None rPr, t_el = make_run(text, sz=sz, bold=bold, underline=ul, italic=ital, color_rgb=color) r_el = etree.SubElement(p_el, f'{{{A}}}r') r_el.append(rPr) r_el.append(t_el) return p_el def empty_para(sz=1800): p_el = etree.Element(f'{{{A}}}p') endPr = etree.SubElement(p_el, f'{{{A}}}endParaRPr') endPr.set('lang', 'en-IN'); endPr.set('sz', str(sz)); endPr.set('dirty', '0') lat = etree.SubElement(endPr, f'{{{A}}}latin') lat.set('typeface', TNR); lat.set('pitchFamily', '18'); lat.set('charset', '0') return p_el def make_title_sp(text, x, y, cx, cy, sz=2800, bold=True, underline=True, ph_type='title'): """Make a title placeholder shape.""" sp = etree.Element(f'{{{P}}}sp') nvSpPr = etree.SubElement(sp, f'{{{P}}}nvSpPr') cNvPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvPr') cNvPr.set('id', '2'); cNvPr.set('name', 'Title 1') cNvSpPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvSpPr') spLocks = etree.SubElement(cNvSpPr, f'{{{A}}}spLocks'); spLocks.set('noGrp', '1') nvPr = etree.SubElement(nvSpPr, f'{{{P}}}nvPr') ph = etree.SubElement(nvPr, f'{{{P}}}ph'); ph.set('type', ph_type) spPr = etree.SubElement(sp, f'{{{P}}}spPr') xfrm = etree.SubElement(spPr, f'{{{A}}}xfrm') off = etree.SubElement(xfrm, f'{{{A}}}off'); off.set('x', str(x)); off.set('y', str(y)) ext = etree.SubElement(xfrm, f'{{{A}}}ext'); ext.set('cx', str(cx)); ext.set('cy', str(cy)) txBody = etree.SubElement(sp, f'{{{P}}}txBody') bodyPr = etree.SubElement(txBody, f'{{{A}}}bodyPr') normAF = etree.SubElement(bodyPr, f'{{{A}}}normAutofit') lstStyle = etree.SubElement(txBody, f'{{{A}}}lstStyle') p_el = etree.SubElement(txBody, f'{{{A}}}p') r_el = etree.SubElement(p_el, f'{{{A}}}r') rPr, t_el = make_run(text, sz=sz, bold=bold, underline=underline) r_el.append(rPr); r_el.append(t_el) return sp def make_content_sp(paragraphs, x, y, cx, cy, sp_id=3): """Make a content placeholder shape with given paragraphs.""" sp = etree.Element(f'{{{P}}}sp') nvSpPr = etree.SubElement(sp, f'{{{P}}}nvSpPr') cNvPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvPr') cNvPr.set('id', str(sp_id)); cNvPr.set('name', f'Content Placeholder {sp_id}') cNvSpPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvSpPr') spLocks = etree.SubElement(cNvSpPr, f'{{{A}}}spLocks'); spLocks.set('noGrp', '1') nvPr = etree.SubElement(nvSpPr, f'{{{P}}}nvPr') ph = etree.SubElement(nvPr, f'{{{P}}}ph'); ph.set('idx', '1') spPr = etree.SubElement(sp, f'{{{P}}}spPr') xfrm = etree.SubElement(spPr, f'{{{A}}}xfrm') off = etree.SubElement(xfrm, f'{{{A}}}off'); off.set('x', str(x)); off.set('y', str(y)) ext = etree.SubElement(xfrm, f'{{{A}}}ext'); ext.set('cx', str(cx)); ext.set('cy', str(cy)) txBody = etree.SubElement(sp, f'{{{P}}}txBody') bodyPr = etree.SubElement(txBody, f'{{{A}}}bodyPr') noAF = etree.SubElement(bodyPr, f'{{{A}}}noAutofit') lstStyle = etree.SubElement(txBody, f'{{{A}}}lstStyle') for p_el in paragraphs: txBody.append(p_el) return sp def make_rect_sp(text, x, y, cx, cy, sp_id=10, sz=2100, text_color='FFFFFF', fill_color=None): """Make a rectangle shape (like the section header bars in Dr. Prachi).""" sp = etree.Element(f'{{{P}}}sp') nvSpPr = etree.SubElement(sp, f'{{{P}}}nvSpPr') cNvPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvPr') cNvPr.set('id', str(sp_id)); cNvPr.set('name', f'Rectangle {sp_id}') cNvSpPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvSpPr') nvPr = etree.SubElement(nvSpPr, f'{{{P}}}nvPr') spPr = etree.SubElement(sp, f'{{{P}}}spPr') xfrm = etree.SubElement(spPr, f'{{{A}}}xfrm') off = etree.SubElement(xfrm, f'{{{A}}}off'); off.set('x', str(x)); off.set('y', str(y)) ext = etree.SubElement(xfrm, f'{{{A}}}ext'); ext.set('cx', str(cx)); ext.set('cy', str(cy)) prstGeom = etree.SubElement(spPr, f'{{{A}}}prstGeom'); prstGeom.set('prst', 'rect') etree.SubElement(prstGeom, f'{{{A}}}avLst') if fill_color: solidFill = etree.SubElement(spPr, f'{{{A}}}solidFill') srgb = etree.SubElement(solidFill, f'{{{A}}}srgbClr'); srgb.set('val', fill_color) ln = etree.SubElement(spPr, f'{{{A}}}ln') lnFill = etree.SubElement(ln, f'{{{A}}}noFill') txBody = etree.SubElement(sp, f'{{{P}}}txBody') bodyPr = etree.SubElement(txBody, f'{{{A}}}bodyPr') bodyPr.set('anchor', 'ctr') lstStyle = etree.SubElement(txBody, f'{{{A}}}lstStyle') p_el = etree.Element(f'{{{A}}}p') pPr = etree.SubElement(p_el, f'{{{A}}}pPr'); pPr.set('algn', 'ctr') r_el = etree.SubElement(p_el, f'{{{A}}}r') rPr, t_el = make_run(text, sz=sz, bold=True, color_rgb=text_color) r_el.append(rPr); r_el.append(t_el) txBody.append(p_el) return sp def make_textbox_sp(paragraphs, x, y, cx, cy, sp_id=20): """Make a standalone text box.""" sp = etree.Element(f'{{{P}}}sp') nvSpPr = etree.SubElement(sp, f'{{{P}}}nvSpPr') cNvPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvPr') cNvPr.set('id', str(sp_id)); cNvPr.set('name', f'TextBox {sp_id}') cNvSpPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvSpPr') cNvSpPr.set('txBox', '1') nvPr = etree.SubElement(nvSpPr, f'{{{P}}}nvPr') spPr = etree.SubElement(sp, f'{{{P}}}spPr') xfrm = etree.SubElement(spPr, f'{{{A}}}xfrm') off = etree.SubElement(xfrm, f'{{{A}}}off'); off.set('x', str(x)); off.set('y', str(y)) ext = etree.SubElement(xfrm, f'{{{A}}}ext'); ext.set('cx', str(cx)); ext.set('cy', str(cy)) prstGeom = etree.SubElement(spPr, f'{{{A}}}prstGeom'); prstGeom.set('prst', 'rect') etree.SubElement(prstGeom, f'{{{A}}}avLst') txBody = etree.SubElement(sp, f'{{{P}}}txBody') bodyPr = etree.SubElement(txBody, f'{{{A}}}bodyPr') bodyPr.set('wrap', 'square') normAF = etree.SubElement(bodyPr, f'{{{A}}}normAutofit') lstStyle = etree.SubElement(txBody, f'{{{A}}}lstStyle') for p_el in paragraphs: txBody.append(p_el) return sp def new_slide(layout): return prs.slides.add_slide(layout) def get_spTree(slide): return slide.shapes._spTree def add_shape(slide, shape_el): get_spTree(slide).append(shape_el) # ============================================================ # Slide construction functions # ============================================================ def title_slide(title_text, subtitle_lines): """Slide 1: Title slide matching Dr. Prachi layout.""" sl = new_slide(TITLE_LAYOUT) tree = get_spTree(sl) # Remove placeholder defaults for sp in list(tree): tag = sp.tag.split('}')[-1] if '}' in sp.tag else sp.tag if tag in ('sp', 'pic'): tree.remove(sp) # Title box title_sp = make_title_sp( title_text, x=emu(1.44), y=emu(1.54), cx=emu(7.12), cy=emu(2.17), sz=2100, bold=True, underline=False, ph_type='ctrTitle' ) tree.append(title_sp) # Subtitle sub_sp = etree.Element(f'{{{P}}}sp') nvSpPr = etree.SubElement(sub_sp, f'{{{P}}}nvSpPr') cNvPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvPr') cNvPr.set('id', '3'); cNvPr.set('name', 'Subtitle 2') cNvSpPr = etree.SubElement(nvSpPr, f'{{{P}}}cNvSpPr') spLocks = etree.SubElement(cNvSpPr, f'{{{A}}}spLocks'); spLocks.set('noGrp', '1') nvPr = etree.SubElement(nvSpPr, f'{{{P}}}nvPr') ph = etree.SubElement(nvPr, f'{{{P}}}ph'); ph.set('type', 'subTitle'); ph.set('idx', '1') spPr = etree.SubElement(sub_sp, f'{{{P}}}spPr') xfrm = etree.SubElement(spPr, f'{{{A}}}xfrm') off = etree.SubElement(xfrm, f'{{{A}}}off'); off.set('x', str(emu(1.5))); off.set('y', str(emu(3.9))) ext = etree.SubElement(xfrm, f'{{{A}}}ext'); ext.set('cx', str(emu(7.0))); ext.set('cy', str(emu(2.8))) txBody = etree.SubElement(sub_sp, f'{{{P}}}txBody') bodyPr = etree.SubElement(txBody, f'{{{A}}}bodyPr') etree.SubElement(bodyPr, f'{{{A}}}normAutofit') lstStyle = etree.SubElement(txBody, f'{{{A}}}lstStyle') for line in subtitle_lines: p_el = etree.Element(f'{{{A}}}p') pPr = etree.SubElement(p_el, f'{{{A}}}pPr'); pPr.set('algn', 'ctr') r_el = etree.SubElement(p_el, f'{{{A}}}r') rPr, t_el = make_run(line, sz=1800, bold=('PROSPECTIVE' in line or 'Hospital' in line)) r_el.append(rPr); r_el.append(t_el) txBody.append(p_el) tree.append(sub_sp) return sl def content_slide(title_text, body_paragraphs, title_sz=2800): """Standard content slide with title + body — exactly like Dr. Prachi.""" sl = new_slide(CONTENT_LAYOUT) tree = get_spTree(sl) for sp in list(tree): tag = sp.tag.split('}')[-1] if '}' in sp.tag else sp.tag if tag in ('sp', 'pic'): tree.remove(sp) # Title title_sp = make_title_sp( title_text, x=emu(1.44), y=emu(0.55), cx=emu(6.75), cy=emu(0.85), sz=title_sz, bold=True, underline=True, ph_type='title' ) tree.append(title_sp) # Content content_sp = make_content_sp( body_paragraphs, x=emu(0.9), y=emu(0.76), cx=emu(8.2), cy=emu(6.0) ) tree.append(content_sp) return sl def rect_header_slide(rect_title, body_paragraphs, rect_x=1.35, rect_y=0.60, rect_w=7.3, rect_h=0.42): """Slide with a colored rectangle header (like Dr. Prachi STUDY DESIGN slide).""" sl = new_slide(CONTENT_LAYOUT) tree = get_spTree(sl) for sp in list(tree): tag = sp.tag.split('}')[-1] if '}' in sp.tag else sp.tag if tag in ('sp', 'pic'): tree.remove(sp) # Rect header rect = make_rect_sp(rect_title, x=emu(rect_x), y=emu(rect_y), cx=emu(rect_w), cy=emu(rect_h), sp_id=5, sz=2100, text_color='FFFFFF', fill_color=None) # Use theme accent fill instead of hardcoded color tree.append(rect) # Content content_sp = make_content_sp( body_paragraphs, x=emu(0.9), y=emu(1.08), cx=emu(8.2), cy=emu(5.9) ) tree.append(content_sp) return sl # ============================================================ # SLIDE 1 — TITLE # ============================================================ title_slide( "EFFECT OF ANTIEPILEPTIC DRUGS ON COGNITIVE FUNCTIONS AND ADVERSE DRUG REACTIONS IN NEWLY DIAGNOSED PAEDIATRIC EPILEPSY", [ "A PROSPECTIVE OBSERVATIONAL STUDY", "", "Dhiraj Hospital, S.B.K.S. Medical Institute & Research Centre", "Piparia, Vadodara", "", "August 2024 – March 2026", "n = 100 Patients | Age Group: 6–16 Years | AED Monotherapy", ] ) # ============================================================ # SLIDE 2 — CONTENTS # ============================================================ content_slide("CONTENTS", [ empty_para(1600), para([("1. Introduction", False, False)], align='just', bullet=False, sz=2000), para([("2. Aim and Objectives", False, False)], align='just', bullet=False, sz=2000), para([("3. Review of Literature", False, False)], align='just', bullet=False, sz=2000), para([("4. Materials and Methods", False, False)], align='just', bullet=False, sz=2000), para([("5. Results", False, False)], align='just', bullet=False, sz=2000), para([("6. Discussion", False, False)], align='just', bullet=False, sz=2000), para([("7. Conclusion", False, False)], align='just', bullet=False, sz=2000), para([("8. References", False, False)], align='just', bullet=False, sz=2000), empty_para(1600), ]) # ============================================================ # SLIDE 3 — INTRODUCTION # ============================================================ content_slide("INTRODUCTION", [ empty_para(1600), para([("Epilepsy is a chronic, non-communicable neurological disorder characterized by a sustained predisposition of the brain to generate epileptic seizures, accompanied by neurobiological, cognitive, psychological, and social consequences.", False, False)], sz=1900), empty_para(1400), para([("According to the World Health Organization (WHO), more than 50 million people globally live with epilepsy, accounting for approximately 0.6–1% of the global population. Nearly 80% reside in low- and middle-income countries where access to diagnosis and treatment is often limited.", False, False)], sz=1900), empty_para(1400), para([("In the paediatric population, epilepsy is one of the most common serious neurological disorders with incidence ranging from 41–187 per 1,00,000 children and prevalence of 3.2–44 per 1000 children.", False, False)], sz=1900), empty_para(1400), para([("Children with epilepsy face difficulties in attention, memory, language acquisition, executive functioning, and social interaction — even in those with well-controlled seizures. These deficits may lead to poor educational outcomes and diminished quality of life.", False, False)], sz=1900), empty_para(1400), para([("Antiepileptic drugs (AEDs) remain the cornerstone of epilepsy management. Approximately 60–70% of patients with newly diagnosed epilepsy achieve satisfactory seizure control with appropriate monotherapy.", False, False)], sz=1900), ]) # ============================================================ # SLIDE 4 — AIM & OBJECTIVES # ============================================================ content_slide("AIM AND OBJECTIVES", [ empty_para(1600), para([("AIM", True, True)], align='center', bullet=False, sz=2000), empty_para(1200), para([("To evaluate the effect of antiepileptic drugs on cognitive functions and to assess the incidence and pattern of adverse drug reactions in newly diagnosed cases of epilepsy in the paediatric age group in a tertiary care teaching rural hospital.", False, False)], sz=2000), empty_para(1400), para([("OBJECTIVES", True, True)], align='center', bullet=False, sz=2000), empty_para(1000), para([("1. To assess the impact of antiepileptic drug therapy on cognitive functions in children with newly diagnosed epilepsy using a standardized cognitive assessment tool (ACE-III).", False, False)], sz=1900), para([("2. To determine the incidence of adverse drug reactions associated with antiepileptic drugs in the paediatric age group.", False, False)], sz=1900), para([("3. To evaluate the pattern and severity of adverse drug reactions occurring during antiepileptic drug therapy.", False, False)], sz=1900), para([("4. To compare baseline and follow-up cognitive function scores after initiation of antiepileptic drug treatment.", False, False)], sz=1900), para([("5. To identify commonly used antiepileptic drugs associated with cognitive impairment and adverse drug reactions in the paediatric population.", False, False)], sz=1900), ]) # ============================================================ # SLIDE 5 — REVIEW OF LITERATURE (key condensed) # ============================================================ content_slide("REVIEW OF LITERATURE", [ empty_para(1400), para([("The global incidence of childhood epilepsy ranges from 50–100 per 1,00,000 children per year, with prevalence estimates of 3–10 per 1,000 children. Higher rates are reported in low- and middle-income countries.", False, False)], sz=1900), empty_para(1000), para([("Cognitive impairment is a major comorbidity of paediatric epilepsy, involving multiple domains — attention, memory, processing speed, executive function, language, verbal fluency, and visuospatial abilities. These deficits may persist even in children with well-controlled seizures.", False, False)], sz=1900), empty_para(1000), para([("Phenobarbital and benzodiazepines are associated with the most significant cognitive adverse effects. Phenytoin and carbamazepine may cause psychomotor slowing and attention deficits. Newer AEDs — lamotrigine, levetiracetam, oxcarbazepine — generally have more favourable cognitive profiles. Topiramate is associated with significant word-finding difficulty and impaired attention.", False, False)], sz=1900), empty_para(1000), para([("Between 40–60% of children receiving AED therapy experience at least one Adverse Drug Reaction. Sedation, dizziness, rash, GI disturbances, and behavioural effects are most common. Polytherapy significantly increases ADR risk.", False, False)], sz=1900), empty_para(1000), para([("Indian studies report ADR profiles comparable to global data, but remain largely cross-sectional with heterogeneous assessment methods. Prospective data in newly diagnosed treatment-naive children from rural settings remain scarce — justifying the present study.", False, False)], sz=1900), ]) # ============================================================ # SLIDE 6 — MATERIALS & METHODS + INCLUSION/EXCLUSION # ============================================================ sl = new_slide(CONTENT_LAYOUT) tree = get_spTree(sl) for sp in list(tree): tag = sp.tag.split('}')[-1] if '}' in sp.tag else sp.tag if tag in ('sp', 'pic'): tree.remove(sp) # Title tree.append(make_title_sp("MATERIALS AND METHODS", x=emu(1.44), y=emu(0.55), cx=emu(6.75), cy=emu(0.85), sz=2800, bold=True, underline=True)) # LEFT column: study design left_paras = [ empty_para(1400), para([("Study Design: ", True, False), ("Prospective, observational, non-interventional study.", False, False)], sz=1800, bullet=False), para([("Setting: ", True, False), ("Paediatrics Dept., Dhiraj Hospital, SBKS Medical Institute & Research Centre, Piparia, Vadodara (Tertiary care rural teaching hospital).", False, False)], sz=1800, bullet=False), para([("Duration: ", True, False), ("18 months — August 2024 to March 2026.", False, False)], sz=1800, bullet=False), para([("Sample: ", True, False), ("100 patients, purposive sampling.", False, False)], sz=1800, bullet=False), empty_para(1200), para([("Cognitive Assessment (ACE-III): ", True, False), ("Addenbrooke's Cognitive Examination-III at Day 0 (baseline) and 12-month follow-up. Five domains evaluated: Attention (18), Memory (26), Fluency (14), Language (26), Visuospatial (16). Total = 100.", False, False)], sz=1800, bullet=False), empty_para(1200), para([("ADR Monitoring: ", True, False), ("WHO–UMC causality assessment scale; Hartwig severity scale.", False, False)], sz=1800, bullet=False), para([("Statistical Methods: ", True, False), ("Paired Student's t-test (within-group); independent Student's t-test (between-group). Microsoft Excel used for data analysis.", False, False)], sz=1800, bullet=False), para([("Ethics: ", True, False), ("Approved by Institutional Ethics Committee. Written informed consent obtained from parents/guardians.", False, False)], sz=1800, bullet=False), ] tree.append(make_content_sp(left_paras, x=emu(0.3), y=emu(0.76), cx=emu(4.4), cy=emu(6.2), sp_id=3)) # RIGHT column: Inclusion/Exclusion as text box right_paras = [ para([("INCLUSION CRITERIA", True, True)], align='center', bullet=False, sz=1900), empty_para(1000), para([("1. Newly diagnosed paediatric epilepsy, age 6–16 years.", False, False)], sz=1750, bullet=False), para([("2. Receiving AED monotherapy.", False, False)], sz=1750, bullet=False), para([("3. Attending OPD/IPD at Dhiraj Hospital.", False, False)], sz=1750, bullet=False), para([("4. Parents/guardians willing to provide informed consent.", False, False)], sz=1750, bullet=False), para([("5. Hemodynamically stable at baseline.", False, False)], sz=1750, bullet=False), empty_para(1200), para([("EXCLUSION CRITERIA", True, True)], align='center', bullet=False, sz=1900), empty_para(1000), para([("1. Progressive neurological disorder, head injury, alcohol/drug abuse, or mental retardation.", False, False)], sz=1750, bullet=False), para([("2. Centrally acting concomitant medications or severe psychiatric disorder.", False, False)], sz=1750, bullet=False), para([("3. Age <6 years or >16 years.", False, False)], sz=1750, bullet=False), para([("4. Parents not willing to provide informed consent.", False, False)], sz=1750, bullet=False), para([("5. Prior AED use or polytherapy.", False, False)], sz=1750, bullet=False), ] tree.append(make_textbox_sp(right_paras, x=emu(5.0), y=emu(0.76), cx=emu(4.7), cy=emu(6.5), sp_id=20)) # ============================================================ # SLIDE 7 — RESULTS: DEMOGRAPHICS # ============================================================ content_slide("RESULTS: DEMOGRAPHIC AND BASELINE CHARACTERISTICS", [ empty_para(1400), para([("A total of 100 newly diagnosed paediatric patients with epilepsy were enrolled. All patients successfully completed the 12-month follow-up — 100% completion rate.", False, False)], sz=1900), empty_para(1200), para([("Gender: ", True, False), ("58 males (58%) and 42 females (42%). Males showed slightly higher mean ACE-III improvement (+3.66) compared to females (+2.79).", False, False)], sz=1900, bullet=False), para([("Age: ", True, False), ("Mean age 10.33 ± 3.04 years (range: 6–16 years). Weight: 35.02 ± 12.05 kg.", False, False)], sz=1900, bullet=False), para([("Vitals at baseline: ", True, False), ("Temperature 36.95 ± 0.30°C | Pulse 89.84 ± 11.24 bpm | Systolic BP 109.91 ± 6.82 mmHg | Diastolic BP 69.23 ± 5.86 mmHg | RR 20.43 ± 3.27/min.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("Cognitive Assessment (ACE-III):", True, True)], align='center', bullet=False, sz=1900), para([("Baseline (Day 0) Mean ACE-III: 73.50 ± 6.70 (range: 53–85)", False, False)], sz=1900, bullet=False), para([("12-Month Mean ACE-III: 76.79 ± 6.80 (range: 60–89)", False, False)], sz=1900, bullet=False), para([("Mean Change: +3.29 ± 3.60 (range: –7 to +10) | p < 0.05 — statistically significant.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("Overall: 77% of patients showed cognitive improvement | 18% declined | 5% showed no change.", False, False)], sz=1900, bullet=False), ], title_sz=2400) # ============================================================ # SLIDE 8 — RESULTS: SEIZURE TYPES + AED PRESCRIPTIONS # ============================================================ content_slide("RESULTS: SEIZURE TYPES AND AED PRESCRIPTION PATTERNS", [ empty_para(1400), para([("Distribution of Seizure Types:", True, True)], align='center', bullet=False, sz=1900), empty_para(1000), para([("Focal seizures were the most common type (52%), followed by Generalised Tonic-Clonic Seizures (27%), Absence (14%), and Myoclonic (7%). Myoclonic seizure patients had the highest baseline scores (78.14) and greatest mean improvement (+4.29). Absence seizure patients started with the lowest baseline (70.79) but achieved a notable +3.71 gain.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("AED Prescription Pattern:", True, True)], align='center', bullet=False, sz=1900), empty_para(1000), para([("All 100 patients (100%) were managed with monotherapy. Second-generation AEDs were prescribed to 55% of patients. Levetiracetam was most frequently prescribed (27%), followed by Carbamazepine (23%), Valproate (17%), Ethosuximide (10%), Lamotrigine (10%), Oxcarbazepine (8%), and Phenytoin (5%).", False, False)], sz=1900, bullet=False), empty_para(1000), para([("Ethosuximide was used in 71.4% of absence epilepsy cases. Carbamazepine (44.2%) and Levetiracetam (40.3%) were the preferred agents for focal seizures. Valproate (71.4%) and Levetiracetam (28.6%) managed myoclonic seizures. AED selection was significantly correlated with seizure type (p < 0.05).", False, False)], sz=1900, bullet=False), ], title_sz=2200) # ============================================================ # SLIDE 9 — RESULTS: COGNITIVE OUTCOMES (ACE-III by AED) # ============================================================ content_slide("RESULTS: COGNITIVE OUTCOMES — ACE-III TOTAL SCORE BY AED", [ empty_para(1400), para([("Lamotrigine (+5.20 ± 2.49) and Ethosuximide (+5.10 ± 2.13) showed the greatest mean cognitive improvement. Oxcarbazepine (+4.75 ± 1.58) had the least variability, suggesting the most consistent benefit. Levetiracetam (+3.67 ± 3.48) showed moderate improvement. Valproate (+2.76 ± 3.31) and Carbamazepine (+1.83 ± 3.98) showed lesser benefit.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("Phenytoin showed zero net cognitive change (0.00 ± 5.57) — the only AED where patients either improved or worsened considerably — with the highest ADR rate of 40%, raising significant concerns about cognitive safety in the paediatric population.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("AED Performance Summary (Mean ACE-III Change):", True, True)], align='center', bullet=False, sz=1900), empty_para(1000), para([("Lamotrigine: +5.20 (ADR 20%) | Ethosuximide: +5.10 (ADR 30%) | Oxcarbazepine: +4.75 (ADR 25%) | Levetiracetam: +3.67 (ADR 22%) | Valproate: +2.76 (ADR 24%) | Carbamazepine: +1.83 (ADR 22%) | Phenytoin: 0.00 (ADR 40%)", False, False)], sz=1900, bullet=False), empty_para(1200), para([("18 patients (18%) experienced a cognitive score decline. 5 patients (5%) had no change. 77 patients (77%) showed improvement over 12 months.", False, False)], sz=1900, bullet=False), ], title_sz=2200) # ============================================================ # SLIDE 10 — RESULTS: ACE-III SUB-DOMAIN ANALYSIS # ============================================================ content_slide("RESULTS: ACE-III SUB-DOMAIN ANALYSIS", [ empty_para(1400), para([("All five ACE-III domains showed improvement over 12 months. Memory (+0.80) and Language (+0.79) showed the greatest gains, reflecting hippocampal and cortical recovery following seizure suppression. Verbal Fluency (68.2%) and Visuospatial function (66.1%) were the most impaired domains at baseline.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("Sub-domain Scores (Day 0 → 12 Months):", True, True)], align='center', bullet=False, sz=1900), empty_para(1000), para([("Attention: 13.90 → 14.46 (+0.56) | Memory: 19.57 → 20.37 (+0.80) | Fluency: 9.55 → 10.10 (+0.55) | Language: 19.91 → 20.70 (+0.79) | Visuospatial: 10.57 → 11.16 (+0.59) | Total: 73.50 → 76.79 (+3.29)", False, False)], sz=1900, bullet=False), empty_para(1200), para([("Domain-Specific Drug Effects: ", True, False), ("Ethosuximide produced the best gains in Attention (+1.40) and Memory (+1.50). Lamotrigine showed the highest Language improvement (+1.70). Phenytoin was the only AED showing decline in Attention (–0.40), Memory (–0.20) and Fluency (–0.40). Oxcarbazepine showed uniformly consistent improvement across all domains.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("Age-group analysis: Children aged 6–9 years (n=46) showed the widest variability (SD 4.52). Adolescents 13–16 years (n=27) had the highest mean improvement (+3.26 ± 1.72).", False, False)], sz=1900, bullet=False), ], title_sz=2400) # ============================================================ # SLIDE 11 — RESULTS: ADR ANALYSIS # ============================================================ content_slide("RESULTS: SAFETY AND ADVERSE DRUG REACTION ANALYSIS", [ empty_para(1400), para([("Out of 100 paediatric patients enrolled, 24 patients (24%) reported at least one adverse drug reaction. The majority were mild to moderate in severity. No Serious Adverse Events (SAEs) occurred and no patient required discontinuation of therapy due to toxicity.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("Types of ADRs: ", True, False), ("Dizziness (7 cases, 29.2%) was the most common, followed by Drowsiness (6 cases, 25%), Rash (6 cases, 25%), GI upset (3 cases, 12.5%), and Irritability (2 cases, 8.3%).", False, False)], sz=1900, bullet=False), empty_para(1200), para([("ADR Rate by AED: ", True, False), ("Phenytoin had the highest ADR rate (40%), followed by Ethosuximide (30%), Oxcarbazepine (25%), Valproate (23.5%), Levetiracetam (22.2%), Carbamazepine (21.7%), and Lamotrigine (20%).", False, False)], sz=1900, bullet=False), empty_para(1200), para([("WHO-UMC Causality: ", True, False), ("All ADRs were classified as either Probable (15) or Possible (9). Dizziness, Drowsiness, and Irritability were classified as Probable; Rash and GI upset as Possible.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("Age-specific pattern: ", True, False), ("Children aged 6–9 years accounted for 50% of all ADRs (12 cases), reflecting higher pharmacokinetic vulnerability due to immature hepatic enzyme systems. The 13–16 years group demonstrated the highest tolerability with only 2 ADRs (10.5%).", False, False)], sz=1900, bullet=False), ], title_sz=2400) # ============================================================ # SLIDE 12 — RESULTS: WHO PRESCRIBING INDICATORS # ============================================================ content_slide("RESULTS: WHO PRESCRIBING INDICATORS", [ empty_para(1400), para([("I. Average number of antiepileptic drugs prescribed per patient: ", True, False), ("1.0 — All patients received monotherapy throughout the study, consistent with the non-interventional design.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("II. Percentage of antiepileptic drugs prescribed by generic name: ", True, False), ("25% — Scope for improvement; higher generic use would reduce treatment cost burden for rural families.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("III. Percentage of antiepileptic drugs prescribed by oral route: ", True, False), ("100% — Optimal compliance with standard outpatient prescribing practice.", False, False)], sz=1900, bullet=False), empty_para(1200), para([("IV. Percentage of drugs prescribed from NLEM 2022: ", True, False), ("72% — Reflects guideline-concordant prescribing in a resource-limited rural tertiary care setting.", False, False)], sz=1900, bullet=False), empty_para(1400), para([("These indicators collectively reflect responsible, evidence-based prescribing practice. The predominance of second-generation AEDs (55%) aligns with global trends favouring newer agents due to improved tolerability and cognitive profiles, consistent with ILAE and NICE NG217 recommendations.", False, False)], sz=1900, bullet=False), ], title_sz=2400) # ============================================================ # SLIDE 13 — DISCUSSION # ============================================================ content_slide("DISCUSSION", [ empty_para(1400), para([("The present study enrolled 100 treatment-naive paediatric patients achieving a 100% completion rate. The mean ACE-III total score improved significantly from 73.50 ± 6.70 to 76.79 ± 6.80 (+3.29 points, p<0.05), with 77% demonstrating cognitive improvement, consistent with prior literature on seizure control benefits.", False, False)], sz=1800, bullet=False), empty_para(1000), para([("Focal seizures were the most prevalent (52%), consistent with epidemiological data reported by Domainska et al. and Kaushik et al. Absence seizures showed the lowest baseline ACE-III (70.79), aligning with the recognised cognitive profile of childhood absence epilepsy.", False, False)], sz=1800, bullet=False), empty_para(1000), para([("Lamotrigine (+5.20) and Ethosuximide (+5.10) showed the highest cognitive gains, consistent with findings of Rosati et al. and Aldenkamp et al. Oxcarbazepine (+4.75, SD 1.58) demonstrated consistent improvement with better tolerability than carbamazepine (Besag et al.). Phenytoin showed zero net cognitive change with the highest ADR rate (40%), supporting its declining role in modern paediatric practice (Chen et al., 2022).", False, False)], sz=1800, bullet=False), empty_para(1000), para([("The overall ADR incidence of 24% is lower than polytherapy-based studies (30–35%) reported by Kaushik et al. and Bansal et al., confirming the safety advantage of monotherapy. Children aged 6–9 years accounted for 50% of ADRs, reflecting pharmacokinetic vulnerability due to immature hepatic enzyme systems (Conway et al., Napoleone et al.).", False, False)], sz=1800, bullet=False), empty_para(1000), para([("The ACE-III was selected for its multi-domain coverage (5 domains), validated sensitivity for detecting mild cognitive impairment (Beishon et al. Cochrane Review; Takenoshita et al.), and suitability for resource-limited settings. Limitations include single-centre design, lack of a control group, small subgroup sizes for phenytoin and oxcarbazepine, and absence of formal socioeconomic assessment.", False, False)], sz=1800, bullet=False), ], title_sz=2800) # ============================================================ # SLIDE 14 — CONCLUSION # ============================================================ content_slide("CONCLUSION", [ empty_para(1400), para([("AED monotherapy in newly diagnosed paediatric epilepsy is associated with statistically significant cognitive improvement and an acceptable safety profile over 12 months in a rural Indian setting.", False, False)], sz=1900, bullet=False), empty_para(1000), para([("Lamotrigine and Ethosuximide demonstrated the most favourable cognitive and safety outcomes, while Phenytoin showed no cognitive benefit and the highest ADR burden (40%), reinforcing its declining role in paediatric practice.", False, False)], sz=1900, bullet=False), empty_para(1000), para([("All ADRs were mild to moderate; no Serious Adverse Events occurred. Children aged 6–9 years exhibited higher ADR susceptibility, emphasizing the need for careful monitoring in this age group.", False, False)], sz=1900, bullet=False), empty_para(1000), para([("55% use of second-generation AEDs reflects guideline-concordant prescribing practice aligned with ILAE and NICE NG217 recommendations. WHO indicators demonstrated 72% NLEM adherence and 100% oral prescribing.", False, False)], sz=1900, bullet=False), empty_para(1000), para([("These findings highlight the importance of:", False, False)], sz=1900, bullet=False), para([(" (a) Baseline cognitive assessment in newly diagnosed children prior to AED initiation.", False, False)], sz=1900, bullet=False), para([(" (b) Preferential use of second-generation AEDs.", False, False)], sz=1900, bullet=False), para([(" (c) Structured age-specific pharmacovigilance.", False, False)], sz=1900, bullet=False), empty_para(1000), para([("Future multicentre prospective studies with larger cohorts, control groups, comprehensive neuropsychological assessments, and socioeconomic variables are warranted.", False, False)], sz=1900, bullet=False), ], title_sz=2800) # Save prs.save(OUT) print(f"Saved: {OUT}") print(f"Total slides: {len(prs.slides)}") import os; print(f"File size: {os.path.getsize(OUT):,} bytes") PYEOF python3 /tmp/workspace/thesis-ppt/build_v3.py

python3 -c " from pptx import Presentation import os prs = Presentation('/tmp/workspace/thesis-ppt/Thesis_AED_Paediatric_FINAL.pptx') print(f'Total slides: {len(prs.slides)}') for i, slide in enumerate(prs.slides, 1): texts = [s.text_frame.text.strip()[:80] for s in slide.shapes if s.has_text_frame and s.text_frame.text.strip()] print(f'Slide {i}: {\" | \".join(texts[:2])}') print() # Verify font is Times New Roman throughout from pptx.oxml.ns import qn count_tnr = 0; count_other = 0 for slide in prs.slides: for shape in slide.shapes: if shape.has_text_frame: for para in shape.text_frame.paragraphs: for run in para.runs: fn = run.font.name if fn == 'Times New Roman': count_tnr += 1 elif fn: count_other += 1 print(f'Times New Roman runs: {count_tnr}, Other font runs: {count_other}') "