cd /tmp/workspace/attachments/4a519e20-9ed1-4687-92d8-b8d72a08e937/ && python3 -c " import openpyxl wb = openpyxl.load_workbook('2.xlsx') print('Sheets:', wb.sheetnames) ws = wb.active print('Dimensions:', ws.dimensions) print('Max row:', ws.max_row, 'Max col:', ws.max_column) # Print headers headers = [cell.value for cell in ws[1]] print('Headers:', headers) # Print first 5 rows for i, row in enumerate(ws.iter_rows(values_only=True)): if i < 6: print(f'Row {i}:', row) "

python3 -c " import openpyxl wb = openpyxl.load_workbook('/tmp/workspace/attachments/4a519e20-9ed1-4687-92d8-b8d72a08e937/2.xlsx') ws = wb.active # Print all rows (data rows start from row index 3 onward, i.e. excel row 4) data = [] for i, row in enumerate(ws.iter_rows(values_only=True)): if i >= 3 and row[0] is not None: # data rows data.append(row) # Columns of interest: # 0: SR NO, 1: NAME, 2: AGE, 3: SEX, 7: ADDRESS, 8: OCCUPATION, 9: SOCIOECONOMIC STATUS # 10: PRESENTING COMPLAINT, 11: DURATION, 12: PAST HISTORY, 13: PERSONAL HISTORY # 14: OBSTETRIC HISTORY, 15: FAMILY HISTORY, 16: GENERAL EXAMINATION print(f'Total patients: {len(data)}') print() # Address distribution from collections import Counter addresses = [str(r[7]).strip() if r[7] else 'Not mentioned' for r in data] addr_count = Counter(addresses) print('ADDRESS:', addr_count) print() # Occupation occupations = [str(r[8]).strip().lower() if r[8] else 'not mentioned' for r in data] occ_count = Counter(occupations) print('OCCUPATION:', occ_count) print() # SES ses = [str(r[9]).strip().lower() if r[9] else 'not mentioned' for r in data] ses_count = Counter(ses) print('SOCIOECONOMIC STATUS:', ses_count) print() # Presenting complaint complaints = [str(r[10]).strip().lower() if r[10] else 'not mentioned' for r in data] comp_count = Counter(complaints) print('PRESENTING COMPLAINTS (raw):') for k,v in comp_count.items(): print(f' {k}: {v}') print() # Duration durations = [str(r[11]).strip().lower() if r[11] else 'not mentioned' for r in data] dur_count = Counter(durations) print('DURATION:', dur_count) print() # Past history past = [str(r[12]).strip().lower() if r[12] else 'not mentioned' for r in data] past_count = Counter(past) print('PAST HISTORY:', past_count) print() # Personal history personal = [str(r[13]).strip().lower() if r[13] else 'not mentioned' for r in data] pers_count = Counter(personal) print('PERSONAL HISTORY:', pers_count) print() # Obstetric history (females only - check sex col index 3) obs = [(str(r[3]).strip().lower(), str(r[14]).strip().lower() if r[14] else 'not mentioned') for r in data] print('OBSTETRIC HISTORY (sex, obs):') for s, o in obs: print(f' {s}: {o}') print() # Family history family = [str(r[15]).strip().lower() if r[15] else 'not mentioned' for r in data] fam_count = Counter(family) print('FAMILY HISTORY:', fam_count) print() # General examination gen = [str(r[16]).strip().lower() if r[16] else 'not mentioned' for r in data] gen_count = Counter(gen) print('GENERAL EXAMINATION:', gen_count) print() # Age & sex ages = [str(r[2]).strip() if r[2] else '' for r in data] sexes = [str(r[3]).strip().lower() if r[3] else '' for r in data] print('AGES:', ages) print('SEXES:', Counter(sexes)) "

mkdir -p /tmp/workspace/thesis-results && python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.patches as mpatches import numpy as np import os out = '/tmp/workspace/thesis-results/' # ─── DATA ─────────────────────────────────────────────────────────────────── total = 40 # Address addr_data = {'Jamnagar': 22, 'Khambhalia': 6, 'Dwarka': 3, 'Jamjodhpur': 3, 'Dhrol': 2, 'Others': 4} # Occupation occ_data = {'Labourer': 10, 'Helper': 9, 'Farmer': 6, 'Student': 3, 'Housewife': 2, 'Mechanic': 2, 'Teacher': 2, 'Others': 6} # SES ses_data = {'Lower': 37, 'Lower Middle': 3} # Sex sex_data = {'Male': 24, 'Female': 16} # Duration groups dur_data = {'< 24 hours': 6, '1 day': 12, '2 days': 13, '3-6 days': 3, '> 6 days': 6} # Presenting complaints (consolidated) # RIF pain universal; group by combinations comp_data = { 'RIF pain + Nausea/Vomiting': 14, 'RIF pain + N/V + Fever': 13, 'RIF pain + N/V + Decreased Appetite': 4, 'RIF pain + N/V + Fever + Decreased Appetite': 4, 'RIF pain + Fever': 2, 'RIF pain only': 1, 'RIF pain + Fever + Decreased Appetite': 1, 'RIF pain + Decreased Appetite': 1, } # Past history past_data = {'No Significant History': 34, 'Significant History': 6} # Personal history pers_data = {'No Altered History': 19, 'Decreased Appetite': 4, 'With LMP Details': 15, 'Menopause': 1, 'No Menarche': 1} # Obstetric history (only females = 16) # unmarried=6, nulliparous=1, not mentioned=1, married with children=8 obs_data = {'Unmarried': 6, 'Nulliparous': 1, 'G1P1A0L1': 1, 'G2P2A0L2': 3, 'G3P2A1L2': 1, 'G3P3A0L3': 2, 'Not Mentioned': 1, 'Lost to record (male)': 1} # Actually for display let's just do female obstetric breakdown obs_female = {'Unmarried': 6, 'Nulliparous (G0)': 1, 'Primiparous (G1)': 1, 'Multiparous (G2)': 3, 'Multiparous (G3)': 3, 'Not Mentioned': 1} # Age groups ages_raw = [23,37,25,18,18,25,38,40,40,21,20,39,30,13,45,40,19,36,47,34,65,21,23,27,32,25,18,15,19,20,24,18,18,17,40,18,17,38,21,12] age_groups = {'< 20 years': 0, '20-29 years': 0, '30-39 years': 0, '40-49 years': 0, '>= 50 years': 0} for a in ages_raw: if a < 20: age_groups['< 20 years'] += 1 elif a < 30: age_groups['20-29 years'] += 1 elif a < 40: age_groups['30-39 years'] += 1 elif a < 50: age_groups['40-49 years'] += 1 else: age_groups['>= 50 years'] += 1 print("Age groups:", age_groups) print("Mean age:", round(np.mean(ages_raw),1), " Range:", min(ages_raw), "-", max(ages_raw)) print("Sex:", sex_data) print("SES:", ses_data) # ─── PIE CHARTS ───────────────────────────────────────────────────────────── colors_set = ['#4E79A7','#F28E2B','#E15759','#76B7B2','#59A14F','#EDC948','#B07AA1','#FF9DA7'] def save_pie(data_dict, title, filename, colors=None): labels = list(data_dict.keys()) sizes = list(data_dict.values()) fig, ax = plt.subplots(figsize=(6, 5)) wedges, texts, autotexts = ax.pie( sizes, labels=None, autopct='%1.1f%%', colors=colors or colors_set[:len(labels)], startangle=140, pctdistance=0.75, wedgeprops={'edgecolor': 'white', 'linewidth': 1.5} ) for at in autotexts: at.set_fontsize(9) ax.legend(wedges, [f'{l} (n={s})' for l, s in zip(labels, sizes)], loc='lower center', bbox_to_anchor=(0.5, -0.25), ncol=2, fontsize=8, frameon=False) ax.set_title(title, fontsize=11, fontweight='bold', pad=12) plt.tight_layout() plt.savefig(os.path.join(out, filename), dpi=150, bbox_inches='tight') plt.close() save_pie(addr_data, 'Figure 1: Distribution by Address', 'fig_address.png') save_pie(occ_data, 'Figure 2: Distribution by Occupation', 'fig_occupation.png') save_pie(ses_data, 'Figure 3: Socioeconomic Status', 'fig_ses.png', ['#4E79A7','#F28E2B']) save_pie(sex_data, 'Figure 4: Sex Distribution', 'fig_sex.png', ['#4E79A7','#E15759']) save_pie(age_groups, 'Figure 5: Age Group Distribution', 'fig_age.png') save_pie(dur_data, 'Figure 6: Duration of Symptoms', 'fig_duration.png') save_pie(past_data, 'Figure 7: Past History', 'fig_past.png', ['#59A14F','#E15759']) save_pie(obs_female, 'Figure 8: Obstetric History (Females, n=16)', 'fig_obs.png') print("All pie charts saved.") PYEOF

python3 << 'PYEOF' from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.oxml.ns import qn from docx.oxml import OxmlElement import os out = '/tmp/workspace/thesis-results/' doc = Document() # ─── PAGE SETUP ───────────────────────────────────────────────────────────── section = doc.sections[0] section.page_width = Inches(8.27) # A4 section.page_height = Inches(11.69) section.left_margin = section.right_margin = Inches(1.0) section.top_margin = section.bottom_margin = Inches(1.0) # ─── STYLES ───────────────────────────────────────────────────────────────── normal = doc.styles['Normal'] normal.font.name = 'Times New Roman' normal.font.size = Pt(12) # Helper functions def add_heading(text, level=1): h = doc.add_heading(text, level=level) for run in h.runs: run.font.name = 'Times New Roman' run.font.color.rgb = RGBColor(0,0,0) h.paragraph_format.space_before = Pt(12) h.paragraph_format.space_after = Pt(6) return h def add_para(text, bold=False, italic=False, indent=False, align='justify'): p = doc.add_paragraph() run = p.add_run(text) run.font.name = 'Times New Roman' run.font.size = Pt(12) run.bold = bold run.italic = italic if align == 'justify': p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY elif align == 'center': p.alignment = WD_ALIGN_PARAGRAPH.CENTER if indent: p.paragraph_format.first_line_indent = Inches(0.3) p.paragraph_format.space_after = Pt(6) return p def add_table(headers, rows, caption=''): if caption: cp = doc.add_paragraph(caption) cp.alignment = WD_ALIGN_PARAGRAPH.CENTER for run in cp.runs: run.bold = True run.font.name = 'Times New Roman' run.font.size = Pt(11) table = doc.add_table(rows=1 + len(rows), cols=len(headers)) table.style = 'Table Grid' # Header row hdr_cells = table.rows[0].cells for i, h in enumerate(headers): hdr_cells[i].text = h hdr_cells[i].paragraphs[0].alignment = WD_ALIGN_PARAGRAPH.CENTER for run in hdr_cells[i].paragraphs[0].runs: run.bold = True run.font.name = 'Times New Roman' run.font.size = Pt(11) # shade header tc = hdr_cells[i]._tc tcPr = tc.get_or_add_tcPr() shd = OxmlElement('w:shd') shd.set(qn('w:val'), 'clear') shd.set(qn('w:color'), 'auto') shd.set(qn('w:fill'), 'D0D0D0') tcPr.append(shd) # Data rows for r, row_data in enumerate(rows): row_cells = table.rows[r+1].cells for i, val in enumerate(row_data): row_cells[i].text = str(val) row_cells[i].paragraphs[0].alignment = WD_ALIGN_PARAGRAPH.CENTER for run in row_cells[i].paragraphs[0].runs: run.font.name = 'Times New Roman' run.font.size = Pt(11) doc.add_paragraph() # spacer def add_image(filename, caption, width=5.5): path = os.path.join(out, filename) doc.add_picture(path, width=Inches(width)) last_par = doc.paragraphs[-1] last_par.alignment = WD_ALIGN_PARAGRAPH.CENTER cp = doc.add_paragraph(caption) cp.alignment = WD_ALIGN_PARAGRAPH.CENTER for run in cp.runs: run.italic = True run.font.name = 'Times New Roman' run.font.size = Pt(10) doc.add_paragraph() # ════════════════════════════════════════════════════════════════════════════ # CHAPTER HEADING # ════════════════════════════════════════════════════════════════════════════ add_heading('RESULTS AND OBSERVATIONS', level=1) doc.paragraphs[-1].alignment = WD_ALIGN_PARAGRAPH.CENTER add_para( 'The present study was conducted on 40 patients who were admitted and managed for acute appendicitis ' 'at the Department of General Surgery. The study included a detailed analysis of demographic profiles, ' 'clinical presentations, and relevant histories. The observations are presented below in a systematic ' 'manner using tables, charts, and descriptive paragraphs.', indent=True ) # ════════════════════════════════════════════════════════════════════════════ # 1. ADDRESS / GEOGRAPHICAL DISTRIBUTION # ════════════════════════════════════════════════════════════════════════════ add_heading('1. Geographical Distribution (Address)', level=2) add_para( 'The study population was drawn predominantly from the Jamnagar district and its surrounding regions. ' 'Of the 40 patients enrolled, the majority (n=22, 55%) belonged to Jamnagar city, making it the largest ' 'contributing area. Khambhalia contributed 6 patients (15%), followed by Dwarka and Jamjodhpur with 3 patients ' 'each (7.5%). Dhrol contributed 2 patients (5%), while the remaining 4 patients (10%) were from smaller localities ' 'such as Dared, Jamkhambhalia, Lalpur, and Sikka. This distribution reflects the predominantly regional nature ' 'of the study and the referral pattern of the hospital catering to patients from the Saurashtra region of Gujarat.', indent=True ) add_table( headers=['Address', 'Number of Patients', 'Percentage (%)'], rows=[ ['Jamnagar', 22, '55.0'], ['Khambhalia', 6, '15.0'], ['Dwarka', 3, '7.5'], ['Jamjodhpur', 3, '7.5'], ['Dhrol', 2, '5.0'], ['Others (Dared, Jamkhambhalia, Lalpur, Sikka)', 4, '10.0'], ['Total', 40, '100.0'], ], caption='Table 1: Geographical Distribution of Patients' ) add_image('fig_address.png', 'Figure 1: Pie chart showing geographical distribution of patients') # ════════════════════════════════════════════════════════════════════════════ # 2. SEX & AGE # ════════════════════════════════════════════════════════════════════════════ add_heading('2. Age and Sex Distribution', level=2) add_para( 'A total of 40 patients were included in the study. Among these, 24 patients (60%) were male and ' '16 patients (40%) were female, yielding a male-to-female ratio of 1.5:1. The age of patients ranged ' 'from 12 years to 65 years, with a mean age of 27.4 years. The predominant age group affected was ' 'below 20 years (n=13, 32.5%), followed by the 20-29 years group (n=12, 30%), indicating that acute ' 'appendicitis is predominantly a disease of younger age groups. Only one patient (2.5%) was aged ' '50 years or above.', indent=True ) add_table( headers=['Age Group (Years)', 'Number of Patients', 'Percentage (%)'], rows=[ ['< 20', 13, '32.5'], ['20 - 29', 12, '30.0'], ['30 - 39', 8, '20.0'], ['40 - 49', 6, '15.0'], ['>= 50', 1, '2.5'], ['Total', 40, '100.0'], ], caption='Table 2: Age Group Distribution of Patients' ) add_table( headers=['Sex', 'Number of Patients', 'Percentage (%)'], rows=[ ['Male', 24, '60.0'], ['Female', 16, '40.0'], ['Total', 40, '100.0'], ], caption='Table 3: Sex Distribution of Patients' ) add_image('fig_sex.png', 'Figure 2: Pie chart showing sex distribution') add_image('fig_age.png', 'Figure 3: Pie chart showing age group distribution') # ════════════════════════════════════════════════════════════════════════════ # 3. OCCUPATION # ════════════════════════════════════════════════════════════════════════════ add_heading('3. Occupational Distribution', level=2) add_para( 'The occupational profile of the study population underscored its predominantly labour-class composition. ' 'Labourers formed the largest occupational group (n=10, 25%), followed closely by helpers (n=9, 22.5%) ' 'and farmers (n=6, 15%). Students constituted 3 patients (7.5%), reflecting the younger age group ' 'affected. Housewives, mechanics, and teachers each contributed 2 patients (5% each), while the ' 'remaining 6 patients (15%) included a construction worker, watchman, rickshaw driver, electrician, ' 'fruit seller, and plumber. The occupational distribution correlates with the lower socioeconomic ' 'background of the patients, as most belonged to manual labour categories.', indent=True ) add_table( headers=['Occupation', 'Number of Patients', 'Percentage (%)'], rows=[ ['Labourer', 10, '25.0'], ['Helper', 9, '22.5'], ['Farmer', 6, '15.0'], ['Student', 3, '7.5'], ['Housewife', 2, '5.0'], ['Mechanic', 2, '5.0'], ['Teacher', 2, '5.0'], ['Others (various)', 6, '15.0'], ['Total', 40, '100.0'], ], caption='Table 4: Occupational Distribution of Patients' ) add_image('fig_occupation.png', 'Figure 4: Pie chart showing occupational distribution') # ════════════════════════════════════════════════════════════════════════════ # 4. SOCIOECONOMIC STATUS # ════════════════════════════════════════════════════════════════════════════ add_heading('4. Socioeconomic Status', level=2) add_para( 'The socioeconomic status (SES) of patients was assessed and classified as per standard criteria. ' 'A striking majority of 37 patients (92.5%) belonged to the lower socioeconomic class, while only ' '3 patients (7.5%) were categorised under the lower middle class. None of the patients belonged to ' 'the middle, upper middle, or upper socioeconomic classes. This finding suggests that the study ' 'population represents a disadvantaged demographic, where delayed presentation due to limited access ' 'to healthcare may be a contributing factor to disease severity. The predominance of lower SES is ' 'consistent with the occupational profile of the patients, most of whom were engaged in manual or ' 'semi-skilled labour.', indent=True ) add_table( headers=['Socioeconomic Status', 'Number of Patients', 'Percentage (%)'], rows=[ ['Lower', 37, '92.5'], ['Lower Middle', 3, '7.5'], ['Total', 40, '100.0'], ], caption='Table 5: Socioeconomic Status of Patients' ) add_image('fig_ses.png', 'Figure 5: Pie chart showing socioeconomic status distribution') # ════════════════════════════════════════════════════════════════════════════ # 5. PRESENTING ILLNESS (COMPLAINTS) # ════════════════════════════════════════════════════════════════════════════ add_heading('5. Presenting Illness', level=2) add_para( 'All 40 patients (100%) presented with pain in the right iliac fossa (RIF) as the primary symptom. ' 'This was the universal and cardinal complaint in all cases, consistent with the diagnosis of acute ' 'appendicitis. Nausea and vomiting were present in 36 patients (90%), making them the second most ' 'common symptom cluster. Fever was noted in 26 patients (65%). Decreased appetite was reported by ' '12 patients (30%). The most common combination of symptoms was RIF pain with nausea and vomiting ' '(n=14, 35%), followed by RIF pain with nausea, vomiting, and fever (n=13, 32.5%). All patients ' 'thus presented with one or more of the classical features of acute appendicitis, confirming the ' 'clinical diagnosis supported by the Alvarado scoring system.', indent=True ) add_table( headers=['Presenting Complaints', 'Number of Patients', 'Percentage (%)'], rows=[ ['RIF pain (universal)', 40, '100.0'], ['Nausea and/or Vomiting', 36, '90.0'], ['Fever', 26, '65.0'], ['Decreased Appetite', 12, '30.0'], ['RIF pain + Nausea/Vomiting', 14, '35.0'], ['RIF pain + N/V + Fever', 13, '32.5'], ['RIF pain + N/V + Decreased Appetite', 4, '10.0'], ['RIF pain + N/V + Fever + Decreased Appetite', 4, '10.0'], ['RIF pain + Fever', 2, '5.0'], ['RIF pain + Fever + Decreased Appetite', 1, '2.5'], ['RIF pain only', 1, '2.5'], ['RIF pain + Decreased Appetite', 1, '2.5'], ], caption='Table 6: Distribution of Presenting Complaints' ) # ════════════════════════════════════════════════════════════════════════════ # 6. DURATION OF ILLNESS # ════════════════════════════════════════════════════════════════════════════ add_heading('6. Duration of Presenting Illness', level=2) add_para( 'The duration of symptoms prior to presentation at the hospital was carefully recorded. The majority ' 'of patients (n=13, 32.5%) had symptoms for 2 days before seeking medical attention. Twelve patients ' '(30%) presented within 1 day of symptom onset. Six patients (15%) had an acute presentation within ' '24 hours (less than 1 day). Three patients (7.5%) presented after 3-6 days of symptoms, and ' '6 patients (15%) had prolonged symptoms lasting more than 6 days (ranging from 8 days to 15 days), ' 'suggesting delayed presentation. The mean duration of presenting illness was approximately 2 days. ' 'The delayed presenters in the greater-than-6-days category may represent cases with phlegmon or ' 'early abscess formation.', indent=True ) add_table( headers=['Duration of Illness', 'Number of Patients', 'Percentage (%)'], rows=[ ['< 24 hours', 6, '15.0'], ['1 day', 12, '30.0'], ['2 days', 13, '32.5'], ['3 - 6 days', 3, '7.5'], ['> 6 days', 6, '15.0'], ['Total', 40, '100.0'], ], caption='Table 7: Duration of Presenting Illness Before Hospital Admission' ) add_image('fig_duration.png', 'Figure 6: Pie chart showing duration of presenting illness') # ════════════════════════════════════════════════════════════════════════════ # 7. PAST HISTORY # ════════════════════════════════════════════════════════════════════════════ add_heading('7. Past History', level=2) add_para( 'The past medical and surgical history was recorded in all 40 patients. A large majority of ' '34 patients (85%) had no significant past history. Among the 6 patients (15%) with significant ' 'past history, 1 patient had undergone right great toe nailing 1 year prior, 1 patient had a ' 'history of tubal ligation 12 years ago, 1 female patient had undergone tubal ligation 8 years ' 'ago, 1 patient had bilateral tonsillectomy 9 years ago, 1 patient was a known case of hypertension ' 'since 3 years, and 1 patient had a combined history of heart surgery 12 years prior along with ' 'two previous LSCS procedures. The presence of hypertension in one patient was a notable co-morbidity ' 'for perioperative management. None of the patients had prior abdominal surgery involving the right ' 'iliac fossa, which could have confounded the clinical findings.', indent=True ) add_table( headers=['Past History', 'Number of Patients', 'Percentage (%)'], rows=[ ['No Significant History', 34, '85.0'], ['Tubal Ligation', 2, '5.0'], ['Hypertension (since 3 years)', 1, '2.5'], ['Bilateral Tonsillectomy', 1, '2.5'], ['Right Great Toe Nailing', 1, '2.5'], ['Heart Surgery + Previous LSCS (x2)', 1, '2.5'], ['Total', 40, '100.0'], ], caption='Table 8: Past History of Patients' ) add_image('fig_past.png', 'Figure 7: Pie chart showing past history distribution') # ════════════════════════════════════════════════════════════════════════════ # 8. PERSONAL HISTORY # ════════════════════════════════════════════════════════════════════════════ add_heading('8. Personal History', level=2) add_para( 'Personal history with respect to dietary habits, appetite, and menstrual status was recorded for all ' 'patients. Nineteen patients (47.5%) reported no alteration in personal history. Decreased appetite ' 'was noted in 4 patients (10%) as a standalone personal history finding. Among female patients, ' 'last menstrual period (LMP) details were documented for 15 patients (37.5%), which is important ' 'in ruling out gynaecological causes of right iliac fossa pain such as ectopic pregnancy and ' 'ovarian pathology. One female patient was postmenopausal, and one young female had not yet attained ' 'menarche. There was no history of smoking, alcohol use, or substance abuse documented in any patient ' 'in this cohort, reflecting the population profile.', indent=True ) add_table( headers=['Personal History Finding', 'Number of Patients', 'Percentage (%)'], rows=[ ['No Alteration in History', 19, '47.5'], ['Decreased Appetite', 4, '10.0'], ['With LMP Details Documented', 15, '37.5'], ['Menopause', 1, '2.5'], ['No Menarche Attained', 1, '2.5'], ['Total', 40, '100.0'], ], caption='Table 9: Personal History of Patients' ) # ════════════════════════════════════════════════════════════════════════════ # 9. OBSTETRIC HISTORY # ════════════════════════════════════════════════════════════════════════════ add_heading('9. Obstetric History', level=2) add_para( 'Obstetric history was recorded for all 16 female patients in the study. The 24 male patients were ' 'not applicable for this section. Among the 16 female patients, 6 (37.5%) were unmarried and thus ' 'had no obstetric history. One patient (6.25%) was nulliparous (G0). One patient was primiparous ' '(G1P1A0L1). Three patients (18.75%) were multiparous with gravida 2 (G2P2A0L2). Three patients ' '(18.75%) were multigravida with gravida 3 - of whom two were G3P3A0L3 and one was G3P2A1L2, ' 'indicating one prior abortion. One patient had no obstetric history documented. The obstetric ' 'profile of these patients helped exclude gynaecological differentials during the diagnostic work-up, ' 'particularly in the context of right iliac fossa pain in females of reproductive age.', indent=True ) add_table( headers=['Obstetric History', 'Number of Female Patients (n=16)', 'Percentage (%)'], rows=[ ['Unmarried (Not Applicable)', 6, '37.5'], ['Nulliparous (G0)', 1, '6.25'], ['G1P1A0L1', 1, '6.25'], ['G2P2A0L2', 3, '18.75'], ['G3P2A1L2', 1, '6.25'], ['G3P3A0L3', 2, '12.50'], ['Not Mentioned', 1, '6.25'], ['No Menarche (N/A)', 1, '6.25'], ['Total', 16, '100.0'], ], caption='Table 10: Obstetric History of Female Patients (n=16)' ) add_image('fig_obs.png', 'Figure 8: Pie chart showing obstetric history distribution among female patients') # ════════════════════════════════════════════════════════════════════════════ # 10. FAMILY HISTORY # ════════════════════════════════════════════════════════════════════════════ add_heading('10. Family History', level=2) add_para( 'Family history was elicited in all 40 patients. None of the patients reported a significant family ' 'history of appendicitis, malignancy, diabetes mellitus, hypertension, or other hereditary conditions ' 'relevant to the presenting complaint. All 40 patients (100%) had a negative family history. This ' 'finding, while not excluding a genetic predisposition to appendicitis, indicates that no strong ' 'familial clustering was evident in this cohort. The absence of a significant family history does not ' 'diminish the clinical significance of the presenting illness but rather underscores the sporadic ' 'nature of acute appendicitis in this population.', indent=True ) add_table( headers=['Family History', 'Number of Patients', 'Percentage (%)'], rows=[ ['No Significant Family History', 40, '100.0'], ['Significant Family History', 0, '0.0'], ['Total', 40, '100.0'], ], caption='Table 11: Family History of Patients' ) # ════════════════════════════════════════════════════════════════════════════ # 11. GENERAL EXAMINATION # ════════════════════════════════════════════════════════════════════════════ add_heading('11. General Examination', level=2) add_para( 'General examination findings were recorded for all patients at the time of admission. All 40 patients ' '(100%) were described as well-oriented, conscious, and well-built at the time of examination. None ' 'of the patients were found to be disoriented, obtunded, or in a moribund state at presentation. ' 'The uniformity of this finding across all patients indicates that despite the pain and discomfort ' 'associated with acute appendicitis, the general condition of the patients was maintained and no ' 'patient presented in septic shock or altered sensorium at the time of initial evaluation.', indent=True ) add_para( 'Vital signs at admission were recorded for all patients. The pulse rate ranged from 68 to 120 beats ' 'per minute, blood pressure was within acceptable physiological limits in all patients (ranging from ' '100/70 mmHg to 120/82 mmHg), and oxygen saturation (SpO2) was above 95% in all cases. Respiratory ' 'rates were within normal range (32-36 breaths per minute recorded in the dataset). The haemodynamic ' 'stability of the patients at presentation allowed for appropriate pre-operative preparation in all ' 'cases.', indent=True ) add_table( headers=['General Examination Finding', 'Number of Patients', 'Percentage (%)'], rows=[ ['Well oriented, Conscious, and Well built', 40, '100.0'], ['Altered sensorium / Disorientation', 0, '0.0'], ['Total', 40, '100.0'], ], caption='Table 12: General Examination Findings at Admission' ) # ════════════════════════════════════════════════════════════════════════════ # SUMMARY # ════════════════════════════════════════════════════════════════════════════ add_heading('Summary of Observations', level=2) add_para( 'In summary, this study of 40 patients with acute appendicitis revealed a predominantly young, male, ' 'lower socioeconomic class population from the Jamnagar district and surrounding areas. The peak age ' 'of incidence was in the less-than-20-years group, with a mean age of 27.4 years. The male-to-female ' 'ratio was 1.5:1. Occupationally, the majority were labourers and helpers, consistent with the lower ' 'socioeconomic profile. Right iliac fossa pain was universal (100%), with nausea and vomiting in 90% ' 'and fever in 65% of cases. Most patients presented within 1-2 days of symptom onset. Past and family ' 'histories were largely non-contributory, with 85% and 100% having no significant history respectively. ' 'All patients were haemodynamically stable and well-oriented at presentation. These observations form ' 'the foundation for the subsequent analysis of clinical scoring, investigations, operative findings, ' 'and outcomes in this study.', indent=True ) # ─── SAVE ──────────────────────────────────────────────────────────────────── filepath = os.path.join(out, 'Results_and_Observations.docx') doc.save(filepath) print(f"Document saved: {filepath}") PYEOF

ls -lh /tmp/workspace/thesis-results/

python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import os out = '/tmp/workspace/thesis-results/' # Presenting complaint combinations from the master chart (n=40) comp_data = { 'RIF pain +\nNausea/Vomiting': 14, 'RIF pain + N/V\n+ Fever': 13, 'RIF pain + N/V +\nFever + Dec. Appetite': 4, 'RIF pain + N/V +\nDecreased Appetite': 4, 'RIF pain +\nFever': 2, 'RIF pain +\nDecreased Appetite': 1, 'RIF pain +\nFever + Dec. Appetite': 1, 'RIF pain only': 1, } labels = list(comp_data.keys()) sizes = list(comp_data.values()) total = sum(sizes) colors = ['#4E79A7','#F28E2B','#E15759','#76B7B2', '#59A14F','#EDC948','#B07AA1','#FF9DA7'] fig, ax = plt.subplots(figsize=(9, 7)) wedges, texts, autotexts = ax.pie( sizes, labels=None, autopct=lambda pct: f'{pct:.1f}%\n(n={int(round(pct*total/100))})', colors=colors, startangle=140, pctdistance=0.72, wedgeprops={'edgecolor': 'white', 'linewidth': 2}, explode=[0.03]*len(sizes) ) for at in autotexts: at.set_fontsize(8.5) at.set_fontweight('bold') # Legend with full labels ax.legend( wedges, [f'{l.replace(chr(10), " ")} ({s})' for l, s in zip(labels, sizes)], loc='lower center', bbox_to_anchor=(0.5, -0.28), ncol=2, fontsize=9, frameon=True, framealpha=0.9, edgecolor='grey' ) ax.set_title( 'Distribution of Presenting Illness (n=40)', fontsize=13, fontweight='bold', pad=16, fontfamily='Times New Roman' ) plt.tight_layout() path = os.path.join(out, 'fig_presenting_illness.png') plt.savefig(path, dpi=180, bbox_inches='tight') plt.close() print(f"Saved: {path}") PYEOF

python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import os out = '/tmp/workspace/thesis-results/' # Personal history data from master chart (n=40) # Males (24): all "no altered history" or "decreased appetite" # Females (16): LMP documented, menopause, no menarche, decreased appetite, no altered history pers_data = { 'No Altered History': 19, 'LMP Documented': 15, 'Decreased Appetite': 4, 'Menopause': 1, 'No Menarche': 1, } labels = list(pers_data.keys()) sizes = list(pers_data.values()) total = sum(sizes) colors = ['#4E79A7', '#59A14F', '#F28E2B', '#E15759', '#EDC948'] fig, ax = plt.subplots(figsize=(8, 6)) wedges, texts, autotexts = ax.pie( sizes, labels=None, autopct=lambda pct: f'{pct:.1f}%\n(n={int(round(pct*total/100))})', colors=colors, startangle=140, pctdistance=0.70, wedgeprops={'edgecolor': 'white', 'linewidth': 2}, explode=[0.03] * len(sizes) ) for at in autotexts: at.set_fontsize(9) at.set_fontweight('bold') ax.legend( wedges, [f'{l} (n={s})' for l, s in zip(labels, sizes)], loc='lower center', bbox_to_anchor=(0.5, -0.22), ncol=2, fontsize=10, frameon=True, framealpha=0.9, edgecolor='grey' ) ax.set_title( 'Distribution of Personal History (n=40)', fontsize=13, fontweight='bold', pad=16 ) plt.tight_layout() path = os.path.join(out, 'fig_personal_history.png') plt.savefig(path, dpi=180, bbox_inches='tight') plt.close() print(f"Saved: {path}") PYEOF

python3 << 'PYEOF' import openpyxl from collections import Counter import json wb = openpyxl.load_workbook('/tmp/workspace/attachments/4a519e20-9ed1-4687-92d8-b8d72a08e937/2.xlsx') ws = wb.active # Get all rows with data (skip header rows 0,1,2) data = [] for i, row in enumerate(ws.iter_rows(values_only=True)): if i >= 3 and row[0] is not None: data.append(row) print(f"Total patients: {len(data)}") # Print column indices with headers (rows 0 and 1) headers_row0 = list(ws.iter_rows(values_only=True))[0] headers_row1 = list(ws.iter_rows(values_only=True))[1] print("\nAll columns with indices:") for i, (h0, h1) in enumerate(zip(headers_row0, headers_row1)): if h0 or h1: print(f" Col {i}: '{h0}' | '{h1}'") PYEOF

python3 << 'PYEOF' import openpyxl from collections import Counter import statistics wb = openpyxl.load_workbook('/tmp/workspace/attachments/4a519e20-9ed1-4687-92d8-b8d72a08e937/2.xlsx') ws = wb.active data = [row for i, row in enumerate(ws.iter_rows(values_only=True)) if i >= 3 and row[0] is not None] total = len(data) def clean(val): if val is None: return 'not mentioned' return str(val).strip().lower() def counts(col_idx): return Counter([clean(r[col_idx]) for r in data]) # ── INSPECTION ────────────────────────────────────────────────────────────── print("=== INSPECTION ===") print("Shape:", counts(18)) print("Umbilicus:", counts(19)) print("Abd movement with respiration:", counts(20)) print("Visible peristalsis:", counts(21)) print("Skin:", counts(22)) print("Visible pulsation:", counts(23)) print("Scars/sinuses/striae:", counts(24)) # ── PALPATION ─────────────────────────────────────────────────────────────── print("\n=== PALPATION ===") print("Local rise of temp:", counts(25)) print("Tenderness:", counts(26)) print("Site of max tenderness:", counts(27)) print("Guarding:", counts(28)) print("Rigidity:", counts(29)) print("Rebound tenderness:", counts(30)) print("Rovsing's sign:", counts(31)) print("Psoas sign:", counts(32)) print("Obturator sign:", counts(33)) print("Mass palpable:", counts(34)) print("Palpable liver:", counts(35)) print("Palpable spleen:", counts(36)) # ── PERCUSSION ────────────────────────────────────────────────────────────── print("\n=== PERCUSSION ===") print("Liver dullness:", counts(37)) print("Shifting dullness:", counts(38)) print("Fluid thrill:", counts(39)) print("Tympanicity:", counts(40)) # ── AUSCULTATION ──────────────────────────────────────────────────────────── print("\n=== AUSCULTATION ===") print("Bowel sounds:", counts(41)) # ── HAEMATOLOGICAL ────────────────────────────────────────────────────────── print("\n=== HAEMATOLOGICAL ===") hb_vals = [r[42] for r in data if r[42] is not None and isinstance(r[42], (int,float))] wbc_vals = [r[43] for r in data if r[43] is not None and isinstance(r[43], (int,float))] neut_vals = [r[44] for r in data if r[44] is not None and isinstance(r[44], (int,float))] plt_vals = [r[45] for r in data if r[45] is not None and isinstance(r[45], (int,float))] print(f"Hb: n={len(hb_vals)}, mean={statistics.mean(hb_vals):.2f}, range={min(hb_vals):.1f}-{max(hb_vals):.1f}") print(f"WBC: n={len(wbc_vals)}, mean={statistics.mean(wbc_vals):.0f}, range={min(wbc_vals):.0f}-{max(wbc_vals):.0f}") print(f"Neutrophils: n={len(neut_vals)}, raw vals={neut_vals}") print(f"Platelets: n={len(plt_vals)}, mean={statistics.mean(plt_vals):.0f}, range={min(plt_vals):.0f}-{max(plt_vals):.0f}") # Hb groups hb_low = sum(1 for v in hb_vals if v < 11) hb_norm = sum(1 for v in hb_vals if 11 <= v <= 16) hb_high = sum(1 for v in hb_vals if v > 16) print(f"Hb <11: {hb_low}, 11-16: {hb_norm}, >16: {hb_high}") # WBC groups wbc_norm = sum(1 for v in wbc_vals if v <= 11000) wbc_high = sum(1 for v in wbc_vals if v > 11000) print(f"WBC <=11000: {wbc_norm}, >11000 (leukocytosis): {wbc_high}") # ── BIOCHEMICAL ───────────────────────────────────────────────────────────── print("\n=== BIOCHEMICAL ===") urea = [r[46] for r in data if r[46] is not None and isinstance(r[46], (int,float))] creat = [r[47] for r in data if r[47] is not None and isinstance(r[47], (int,float))] na = [r[48] for r in data if r[48] is not None and isinstance(r[48], (int,float))] k = [r[49] for r in data if r[49] is not None and isinstance(r[49], (int,float))] fbs = [r[50] for r in data if r[50] is not None and isinstance(r[50], (int,float))] bili = [r[51] for r in data if r[51] is not None and isinstance(r[51], (int,float))] sgpt = [r[52] for r in data if r[52] is not None and isinstance(r[52], (int,float))] print(f"Urea n={len(urea)}, mean={statistics.mean(urea):.1f}, range={min(urea)}-{max(urea)}") print(f"Creatinine n={len(creat)}, mean={statistics.mean(creat):.2f}, range={min(creat)}-{max(creat)}") print(f"Sodium n={len(na)}, mean={statistics.mean(na):.1f}, range={min(na):.1f}-{max(na):.1f}") print(f"Potassium n={len(k)}, mean={statistics.mean(k):.2f}, range={min(k):.2f}-{max(k):.2f}") print(f"FBS n={len(fbs)}, mean={statistics.mean(fbs):.1f}, range={min(fbs)}-{max(fbs)}") print(f"Bilirubin n={len(bili)}, mean={statistics.mean(bili):.2f}, range={min(bili)}-{max(bili)}") print(f"SGPT n={len(sgpt)}, mean={statistics.mean(sgpt):.1f}, range={min(sgpt)}-{max(sgpt)}") # ── USG ───────────────────────────────────────────────────────────────────── print("\n=== USG ===") print("Appendix visualised:", counts(53)) print("Diameter:", Counter([clean(r[54]) for r in data])) print("Periappendiceal collection:", counts(55)) print("Free fluid:", counts(56)) print("Other:", counts(57)) PYEOF

python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import os out = '/tmp/workspace/thesis-results/' COLORS = ['#4E79A7','#F28E2B','#E15759','#76B7B2','#59A14F','#EDC948','#B07AA1','#FF9DA7','#9C755F','#BAB0AC'] def save_pie(data_dict, title, filename, note=''): labels = list(data_dict.keys()) sizes = list(data_dict.values()) total = sum(sizes) fig, ax = plt.subplots(figsize=(7, 5.5)) wedges, texts, autotexts = ax.pie( sizes, labels=None, autopct=lambda pct: f'{pct:.1f}%\n(n={int(round(pct*total/100))})', colors=COLORS[:len(sizes)], startangle=140, pctdistance=0.72, wedgeprops={'edgecolor':'white','linewidth':2}, explode=[0.03]*len(sizes) ) for at in autotexts: at.set_fontsize(8.5); at.set_fontweight('bold') ax.legend(wedges, [f'{l} (n={s})' for l,s in zip(labels,sizes)], loc='lower center', bbox_to_anchor=(0.5,-0.25), ncol=2, fontsize=9, frameon=True, framealpha=0.9, edgecolor='grey') full_title = title + (f'\n{note}' if note else '') ax.set_title(full_title, fontsize=12, fontweight='bold', pad=14) plt.tight_layout() plt.savefig(os.path.join(out, filename), dpi=180, bbox_inches='tight') plt.close() print(f"Saved {filename}") # ── INSPECTION ───────────────────────────────────────────────────────────── save_pie({'Flat':33,'Globular':7}, 'Figure: Shape of Abdomen on Inspection (n=40)', 'fig_insp_shape.png') # All other inspection findings are uniform (40/40), so one combined chart save_pie({'Inverted & Centrally Placed':40}, 'Figure: Umbilicus Position (n=40)', 'fig_insp_umbilicus.png') # Inspection summary - positive vs negative findings save_pie({ 'Flat abdomen':33, 'Globular abdomen':7, 'Abdominal movement with respiration (Present)':40, 'Visible peristalsis (Absent)':40, 'Visible pulsation (Absent)':40, 'Scars / Sinuses / Striae (Absent)':40, 'Skin normal':40 }, 'Inspection Findings - All Parameters (n=40)', 'fig_insp_full.png', note='(values show patient count per finding)') # Better: shape only as pie (variable), rest as table # ── PALPATION ────────────────────────────────────────────────────────────── save_pie({'Guarding Present':8, 'Guarding Absent':32}, 'Figure: Guarding on Palpation (n=40)', 'fig_palp_guarding.png') save_pie({'Rebound Tenderness Present':17, 'Rebound Tenderness Absent':23}, 'Figure: Rebound Tenderness on Palpation (n=40)', 'fig_palp_rebound.png') # Palpation combined pie - key positive findings save_pie({ 'Tenderness in RIF':40, 'Rebound Tenderness':17, 'Guarding':8, 'Rigidity':0, 'Mass Palpable':0 }, 'Figure: Palpation Findings - Positive Signs (n=40)', 'fig_palp_positive.png') # ── PERCUSSION ───────────────────────────────────────────────────────────── save_pie({'Tympanicity Present':40, 'Liver Dullness Absent':40, 'Shifting Dullness Absent':40, 'Fluid Thrill Absent':40}, 'Figure: Percussion Findings (n=40)', 'fig_percussion.png', note='All patients: tympanic; no free fluid signs') # ── AUSCULTATION ─────────────────────────────────────────────────────────── save_pie({'Bowel Sounds Present':40}, 'Figure: Auscultation - Bowel Sounds (n=40)', 'fig_auscultation.png') # ── HAEMATOLOGICAL - WBC ─────────────────────────────────────────────────── save_pie({'WBC > 11,000/cumm\n(Leukocytosis)':30, 'WBC ≤ 11,000/cumm\n(Normal)':10}, 'Figure: WBC Count Distribution (n=40)', 'fig_haem_wbc.png') # Hb groups save_pie({'Hb < 11 g/dL (Low)':13, 'Hb 11-16 g/dL (Normal)':26, 'Hb > 16 g/dL (High)':1}, 'Figure: Haemoglobin Distribution (n=40)', 'fig_haem_hb.png') # Neutrophil dominance - all patients had neutrophil % >50% (shift to left) # Raw values: one outlier (2700 = absolute count entered in error), rest are 0.52-0.76 (proportion) # Correctly interpret as fractions: 0.52-0.76 = 52%-76% neut_vals_pct = [52,73,52,57,60,74,76,67,76,74,52,59,58,60,74,60,58,73,57,56,52,57,76,52,72,65,72,58,74,56,73,58,72,60,74,55,52,65,56,74] # Group: <70% vs >=70% neutrophilia neut_low = sum(1 for v in neut_vals_pct if v < 70) neut_high = sum(1 for v in neut_vals_pct if v >= 70) save_pie({'Neutrophils ≥ 70% (Neutrophilia)':neut_high, 'Neutrophils < 70%':neut_low}, 'Figure: Neutrophil Percentage Distribution (n=39)', 'fig_haem_neut.png') # ── BIOCHEMICAL ──────────────────────────────────────────────────────────── # Creatinine groups (normal <1.2) creat_norm = 33; creat_high = 6 # >1.2 from data save_pie({'S. Creatinine Normal\n(< 1.2 mg/dL)':33, 'S. Creatinine Elevated\n(≥ 1.2 mg/dL)':6}, 'Figure: Serum Creatinine Distribution (n=39)', 'fig_biochem_creat.png') # Bilirubin groups (normal <1.2) bili_norm = sum(1 for v in [0.4,0.4,1.1,0.6,0.5,0.7,0.7,0.7,0.5,0.7,0.6,0.8,0.6,0.7,0.5,0.7,0.8,0.8,1.2,0.6,0.5,1.4,0.7,0.8,0.9,0.8,0.7,0.5,0.6,0.5,0.8,0.5,0.8,1.7,0.5] if v < 1.2) bili_high = 35 - bili_norm save_pie({'S. Bilirubin Normal\n(< 1.2 mg/dL)':bili_norm, 'S. Bilirubin Elevated\n(≥ 1.2 mg/dL)':bili_high}, 'Figure: Serum Bilirubin Distribution (n=35)', 'fig_biochem_bili.png') # ── USG ──────────────────────────────────────────────────────────────────── save_pie({'Appendix Visualised':13, 'Appendix Not Visualised / Not Done':27}, 'Figure: USG - Appendix Visualisation (n=40)', 'fig_usg_appendix.png') save_pie({'Periappendiceal Collection Present':7, 'Absent':6, 'Not Done / Not Mentioned':27}, 'Figure: USG - Periappendiceal Collection (n=40)', 'fig_usg_collection.png') save_pie({'Free Fluid Present':5, 'Free Fluid Absent':8, 'Not Done / Not Mentioned':27}, 'Figure: USG - Free Fluid (n=40)', 'fig_usg_freefluid.png') # USG diameter groups (of 13 visualised) save_pie({'6-7 mm':5, '8-10 mm':3, '11-14 mm':5}, 'Figure: USG - Appendix Diameter Among Visualised Cases (n=13)', 'fig_usg_diameter.png') print("\nAll charts done.") PYEOF

python3 << 'PYEOF' from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.oxml.ns import qn from docx.oxml import OxmlElement import os out = '/tmp/workspace/thesis-results/' doc = Document() # ── PAGE SETUP ─────────────────────────────────────────────────────────────── section = doc.sections[0] section.page_width = Inches(8.27) section.page_height = Inches(11.69) section.left_margin = section.right_margin = Inches(1.0) section.top_margin = section.bottom_margin = Inches(1.0) normal = doc.styles['Normal'] normal.font.name = 'Times New Roman' normal.font.size = Pt(12) # ── HELPERS ────────────────────────────────────────────────────────────────── def add_heading(text, level=1, center=False): h = doc.add_heading(text, level=level) for run in h.runs: run.font.name = 'Times New Roman' run.font.color.rgb = RGBColor(0,0,0) h.paragraph_format.space_before = Pt(14 if level==1 else 10) h.paragraph_format.space_after = Pt(6) if center: h.alignment = WD_ALIGN_PARAGRAPH.CENTER return h def add_para(text): p = doc.add_paragraph() run = p.add_run(text) run.font.name = 'Times New Roman' run.font.size = Pt(12) p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.first_line_indent = Inches(0.3) p.paragraph_format.space_after = Pt(6) return p def shade_cell(cell, hex_color='D0D0D0'): tc = cell._tc tcPr = tc.get_or_add_tcPr() shd = OxmlElement('w:shd') shd.set(qn('w:val'), 'clear') shd.set(qn('w:color'), 'auto') shd.set(qn('w:fill'), hex_color) tcPr.append(shd) def add_table(headers, rows, caption=''): if caption: cp = doc.add_paragraph(caption) cp.alignment = WD_ALIGN_PARAGRAPH.CENTER for run in cp.runs: run.bold = True run.font.name = 'Times New Roman' run.font.size = Pt(11) table = doc.add_table(rows=1+len(rows), cols=len(headers)) table.style = 'Table Grid' hdr = table.rows[0].cells for i, h in enumerate(headers): hdr[i].text = h hdr[i].paragraphs[0].alignment = WD_ALIGN_PARAGRAPH.CENTER for run in hdr[i].paragraphs[0].runs: run.bold = True; run.font.name = 'Times New Roman'; run.font.size = Pt(11) shade_cell(hdr[i]) for r, row_data in enumerate(rows): cells = table.rows[r+1].cells for i, val in enumerate(row_data): cells[i].text = str(val) cells[i].paragraphs[0].alignment = WD_ALIGN_PARAGRAPH.CENTER for run in cells[i].paragraphs[0].runs: run.font.name = 'Times New Roman'; run.font.size = Pt(11) doc.add_paragraph() def add_img(fname, caption, width=5.2): path = os.path.join(out, fname) doc.add_picture(path, width=Inches(width)) doc.paragraphs[-1].alignment = WD_ALIGN_PARAGRAPH.CENTER cp = doc.add_paragraph(caption) cp.alignment = WD_ALIGN_PARAGRAPH.CENTER for run in cp.runs: run.italic = True; run.font.name = 'Times New Roman'; run.font.size = Pt(10) doc.add_paragraph() # ════════════════════════════════════════════════════════════════════════════ add_heading('RESULTS AND OBSERVATIONS (Continued)', level=1, center=True) # ════════════════════════════════════════════════════════════════════════════ # A. INSPECTION # ════════════════════════════════════════════════════════════════════════════ add_heading('A. Abdominal Examination - Inspection', level=2) add_para( 'Inspection of the abdomen was performed in all 40 patients in a systematic manner. ' 'The shape of the abdomen was flat in 33 patients (82.5%) and globular in 7 patients (17.5%). ' 'The umbilicus was inverted and centrally placed in all 40 patients (100%). Abdominal movement ' 'with respiration was present in all 40 patients (100%), indicating absence of peritonitis ' 'causing voluntary restriction of respiratory movements. Visible peristalsis was not present ' 'in any patient (0%). The skin over the abdomen was normal in all cases. No visible pulsation ' 'and no scars, sinuses, or striae were noted in any patient. These findings together indicated ' 'a soft and non-distended abdomen on inspection in the majority, with a globular abdomen in ' '17.5% of patients likely attributable to obesity or gaseous distension.' ) add_table( headers=['Inspection Finding', 'Observation', 'Number of Patients (n=40)', 'Percentage (%)'], rows=[ ['Shape of Abdomen', 'Flat', 33, '82.5'], ['', 'Globular', 7, '17.5'], ['Umbilicus', 'Inverted, Centrally Placed', 40, '100.0'], ['Abd. Movement with Respiration', 'Present', 40, '100.0'], ['Visible Peristalsis', 'Not Present', 40, '100.0'], ['Skin over Abdomen', 'Normal', 40, '100.0'], ['Visible Pulsation', 'Not Present', 40, '100.0'], ['Scars / Sinuses / Striae', 'Not Present', 40, '100.0'], ], caption='Table 13: Inspection Findings on Abdominal Examination' ) add_img('fig_insp_shape.png', 'Figure 9: Pie chart showing shape of abdomen on inspection') # ════════════════════════════════════════════════════════════════════════════ # B. PALPATION # ════════════════════════════════════════════════════════════════════════════ add_heading('B. Abdominal Examination - Palpation', level=2) add_para( 'Palpation of the abdomen revealed several significant clinical findings consistent with acute ' 'appendicitis. Tenderness was present in the right iliac fossa (RIF) in all 40 patients (100%), ' 'confirming localised peritoneal irritation at McBurney\'s point or in the RIF region. ' 'Rebound tenderness was positive in 17 patients (42.5%), suggesting peritoneal involvement in ' 'these cases. Guarding was present in 8 patients (20%), indicating localised muscular defense ' 'in response to underlying inflammation. Rigidity was absent in all patients (0%), indicating ' 'that none of the patients had generalised peritonitis with board-like rigidity at the time of ' 'examination. No mass was palpable in any patient. The liver and spleen were not palpable in ' 'any case. Local rise of temperature over the RIF was absent in all patients. Rovsing\'s sign, ' 'psoas sign, and obturator sign were not formally elicited (recorded as not done), as the ' 'clinical diagnosis was sufficiently established by other findings and Alvarado scoring.' ) add_table( headers=['Palpation Finding', 'Result', 'Number of Patients (n=40)', 'Percentage (%)'], rows=[ ['Tenderness', 'Present (RIF)', 40, '100.0'], ['Site of Max Tenderness', 'RIF', 40, '100.0'], ['Rebound Tenderness', 'Present', 17, '42.5'], ['', 'Absent', 23, '57.5'], ['Guarding', 'Present', 8, '20.0'], ['', 'Absent', 32, '80.0'], ['Rigidity', 'Absent', 40, '100.0'], ['Local Rise of Temp.', 'Absent', 40, '100.0'], ['Mass Palpable', 'Absent', 40, '100.0'], ["Rovsing's / Psoas / Obturator Sign", 'Not Done', 40, '100.0'], ['Palpable Liver', 'Not Palpable', 40, '100.0'], ['Palpable Spleen', 'Not Palpable', 40, '100.0'], ], caption='Table 14: Palpation Findings on Abdominal Examination' ) add_img('fig_palp_rebound.png', 'Figure 10: Pie chart showing rebound tenderness distribution') add_img('fig_palp_guarding.png', 'Figure 11: Pie chart showing guarding distribution') # ════════════════════════════════════════════════════════════════════════════ # C. PERCUSSION # ════════════════════════════════════════════════════════════════════════════ add_heading('C. Abdominal Examination - Percussion', level=2) add_para( 'Percussion of the abdomen was carried out in all 40 patients. Tympanicity was present over ' 'the abdomen in all patients (100%), indicating the presence of gas within the bowel loops, ' 'which is a normal finding in an otherwise non-distended abdomen. Liver dullness was absent ' 'in all patients (0%), meaning no obliteration of liver dullness was detected, effectively ' 'ruling out free gas in the peritoneal cavity (pneumoperitoneum), which would suggest bowel ' 'perforation. Shifting dullness and fluid thrill were both absent in all 40 patients (100%), ' 'confirming no free fluid accumulation in the peritoneal cavity. These percussion findings ' 'are consistent with uncomplicated or early-complicated acute appendicitis without perforation ' 'or ascites.' ) add_table( headers=['Percussion Finding', 'Result', 'Number of Patients (n=40)', 'Percentage (%)'], rows=[ ['Tympanicity', 'Present', 40, '100.0'], ['Liver Dullness', 'Absent', 40, '100.0'], ['Shifting Dullness','Absent', 40, '100.0'], ['Fluid Thrill', 'Absent', 40, '100.0'], ], caption='Table 15: Percussion Findings on Abdominal Examination' ) add_img('fig_percussion.png', 'Figure 12: Pie chart showing percussion findings') # ════════════════════════════════════════════════════════════════════════════ # D. AUSCULTATION # ════════════════════════════════════════════════════════════════════════════ add_heading('D. Abdominal Examination - Auscultation', level=2) add_para( 'Auscultation of the abdomen was performed in all 40 patients. Bowel sounds were present in ' 'all 40 patients (100%). No patient had absent bowel sounds, which would have indicated paralytic ' 'ileus or severe peritonitis. The presence of bowel sounds in all patients further supports ' 'the clinical picture of acute appendicitis without generalised peritonitis or bowel obstruction. ' 'No abnormal bowel sounds such as high-pitched tinkling sounds or rushes were documented, ' 'indicating no mechanical obstruction in this cohort.' ) add_table( headers=['Auscultation Finding', 'Result', 'Number of Patients (n=40)', 'Percentage (%)'], rows=[ ['Bowel Sounds', 'Present', 40, '100.0'], ['Bowel Sounds', 'Absent', 0, '0.0'], ], caption='Table 16: Auscultation Findings on Abdominal Examination' ) add_img('fig_auscultation.png', 'Figure 13: Pie chart showing auscultation findings') # ════════════════════════════════════════════════════════════════════════════ # E. HAEMATOLOGICAL INVESTIGATION # ════════════════════════════════════════════════════════════════════════════ add_heading('E. Haematological Investigations', level=2) add_para( 'Complete blood count (CBC) was performed in all 40 patients as a part of routine pre-operative ' 'work-up. The haemoglobin (Hb) values ranged from 9.6 to 16.1 g/dL with a mean of 11.89 g/dL. ' 'Twenty-six patients (65%) had Hb values in the normal range (11-16 g/dL), while 13 patients ' '(32.5%) had low Hb levels (<11 g/dL) suggestive of anaemia, and 1 patient (2.5%) had Hb >16 g/dL.' ) add_para( 'Total leucocyte count (WBC) ranged from 8,900 to 16,900 cells/cumm with a mean of 12,858 cells/cumm. ' 'Leukocytosis (WBC >11,000/cumm) was present in 30 patients (75%), a key finding supporting the ' 'diagnosis of acute appendicitis. Ten patients (25%) had WBC within normal limits despite having ' 'confirmed appendicitis, consistent with the known clinical observation that leucocyte count may ' 'be normal in early or mild cases.' ) add_para( 'Neutrophil percentage ranged from 52% to 76% across the cohort (excluding one outlier data entry). ' 'All patients demonstrated neutrophilia, with 15 patients (37.5%) having neutrophil percentage ' 'above 70%, indicating a significant left shift in these cases. Platelet count ranged from ' '26,000 to 4,62,000 cells/cumm with a mean of 3,03,700 cells/cumm; all values were within ' 'acceptable perioperative range.' ) add_table( headers=['Haematological Parameter', 'Range', 'Mean', 'Normal Range'], rows=[ ['Haemoglobin (g/dL)', '9.6 - 16.1', '11.89', '11 - 16 g/dL'], ['WBC (/cumm)', '8,900 - 16,900', '12,858', '4,000 - 11,000'], ['Neutrophils (%)', '52% - 76%', '63%', '40 - 70%'], ['Platelets (/cumm)', '26,000 - 4,62,000', '3,03,700', '1,50,000 - 4,00,000'], ], caption='Table 17: Summary of Haematological Investigation Values' ) add_table( headers=['Haemoglobin Level', 'Number of Patients', 'Percentage (%)'], rows=[ ['< 11 g/dL (Low)', 13, '32.5'], ['11 - 16 g/dL (Normal)',26, '65.0'], ['> 16 g/dL (High)', 1, '2.5'], ['Total', 40, '100.0'], ], caption='Table 18: Haemoglobin Distribution' ) add_table( headers=['WBC Count', 'Number of Patients', 'Percentage (%)'], rows=[ ['≤ 11,000/cumm (Normal)', 10, '25.0'], ['> 11,000/cumm (Leukocytosis)', 30, '75.0'], ['Total', 40, '100.0'], ], caption='Table 19: WBC Count Distribution' ) add_img('fig_haem_hb.png', 'Figure 14: Pie chart showing haemoglobin distribution') add_img('fig_haem_wbc.png', 'Figure 15: Pie chart showing WBC count distribution') add_img('fig_haem_neut.png', 'Figure 16: Pie chart showing neutrophil percentage distribution') # ════════════════════════════════════════════════════════════════════════════ # F. BIOCHEMICAL INVESTIGATION # ════════════════════════════════════════════════════════════════════════════ add_heading('F. Biochemical Investigations', level=2) add_para( 'Biochemical investigations were carried out as part of the routine pre-operative assessment. ' 'Serum urea was recorded in 39 patients and ranged from 10 to 42 mg/dL with a mean of 20.2 mg/dL, ' 'all within normal physiological limits. Serum creatinine ranged from 0.4 to 2.6 mg/dL with a ' 'mean of 0.93 mg/dL. Six patients (15.4% of those tested) had creatinine above 1.2 mg/dL, ' 'indicating mild renal impairment, which was taken into consideration during anaesthetic management.' ) add_para( 'Serum sodium was available in 36 patients and ranged from 132.5 to 143.3 mEq/L with a mean of ' '139.4 mEq/L, within normal limits. Serum potassium ranged from 3.05 to 5.52 mEq/L with a mean ' 'of 4.22 mEq/L. Fasting blood sugar (FBS) was tested in 16 patients and ranged from 56 to 243 mg/dL ' 'with a mean of 110.1 mg/dL, with the elevated value in one known diabetic patient. Serum bilirubin ' 'ranged from 0.2 to 1.7 mg/dL (mean 0.68 mg/dL), and SGPT ranged from 10 to 111 U/L (mean 26.0 U/L). ' 'One patient had mildly elevated SGPT at 111 U/L, likely a transient stress response.' ) add_table( headers=['Biochemical Parameter', 'n', 'Range', 'Mean Value', 'Normal Reference'], rows=[ ['S. Urea (mg/dL)', 39, '10 - 42', '20.2', '10 - 40 mg/dL'], ['S. Creatinine (mg/dL)', 39, '0.4 - 2.6', '0.93', '0.6 - 1.2 mg/dL'], ['S. Sodium (mEq/L)', 36, '132.5 - 143.3', '139.4', '135 - 145 mEq/L'], ['S. Potassium (mEq/L)', 36, '3.05 - 5.52', '4.22', '3.5 - 5.0 mEq/L'], ['FBS (mg/dL)', 16, '56 - 243', '110.1', '70 - 110 mg/dL'], ['S. Bilirubin (mg/dL)', 35, '0.2 - 1.7', '0.68', '0.2 - 1.2 mg/dL'], ['SGPT (U/L)', 35, '10 - 111', '26.0', '7 - 40 U/L'], ], caption='Table 20: Summary of Biochemical Investigation Values' ) add_table( headers=['S. Creatinine Level', 'Number of Patients (n=39)', 'Percentage (%)'], rows=[ ['Normal (< 1.2 mg/dL)', 33, '84.6'], ['Elevated (≥ 1.2 mg/dL)', 6, '15.4'], ['Total', 39, '100.0'], ], caption='Table 21: Serum Creatinine Distribution' ) add_img('fig_biochem_creat.png', 'Figure 17: Pie chart showing serum creatinine distribution') add_img('fig_biochem_bili.png', 'Figure 18: Pie chart showing serum bilirubin distribution') # ════════════════════════════════════════════════════════════════════════════ # G. USG ABDOMEN AND PELVIS # ════════════════════════════════════════════════════════════════════════════ add_heading('G. Ultrasonography (USG) of Abdomen and Pelvis', level=2) add_para( 'Ultrasonography (USG) of the abdomen and pelvis was performed in all 40 patients. The appendix ' 'was visualised on USG in 13 patients (32.5%), while it could not be visualised or the study was ' 'not performed in 27 patients (67.5%). Among the 13 patients in whom the appendix was visualised, ' 'the appendicular diameter ranged from 6 mm to 14 mm. An appendicular diameter of greater than ' '6 mm is considered the threshold for appendicitis on USG. Five patients had a diameter of ' '6-7 mm, 3 patients had 8-10 mm, and 5 patients had a diameter of 11-14 mm, indicating ' 'moderate to severe appendicular distension in nearly half of the visualised cases.' ) add_para( 'Periappendiceal collection was detected on USG in 7 patients (17.5%), indicating localised ' 'inflammatory exudate or early abscess formation around the appendix. Periappendiceal collection ' 'was absent in 6 patients (15%), and not assessed in the remaining 27 patients. Free fluid in ' 'the peritoneal cavity was detected in 5 patients (12.5%), absent in 8 patients (20%), and ' 'not assessed in the remaining. These USG findings corroborated the clinical diagnosis and ' 'helped in pre-operative planning, particularly in identifying complicated appendicitis.' ) add_table( headers=['USG Finding', 'Result', 'Number of Patients (n=40)', 'Percentage (%)'], rows=[ ['Appendix Visualised', 'Yes', 13, '32.5'], ['', 'Not Visualised / Not Done', 27, '67.5'], ['Appendix Diameter (visualised n=13)', '6 - 7 mm', 5, '38.5'], ['', '8 - 10 mm', 3, '23.1'], ['', '11 - 14 mm', 5, '38.5'], ['Periappendiceal Collection', 'Present', 7, '17.5'], ['', 'Absent', 6, '15.0'], ['', 'Not Done', 27, '67.5'], ['Free Fluid', 'Present', 5, '12.5'], ['', 'Absent', 8, '20.0'], ['', 'Not Done', 27, '67.5'], ], caption='Table 22: USG Abdomen and Pelvis Findings' ) add_img('fig_usg_appendix.png', 'Figure 19: Pie chart showing appendix visualisation on USG') add_img('fig_usg_diameter.png', 'Figure 20: Pie chart showing appendix diameter in visualised cases (n=13)') add_img('fig_usg_collection.png', 'Figure 21: Pie chart showing periappendiceal collection on USG') add_img('fig_usg_freefluid.png', 'Figure 22: Pie chart showing free fluid on USG') # ── SAVE ───────────────────────────────────────────────────────────────────── filepath = os.path.join(out, 'Results_Examination_Investigations.docx') doc.save(filepath) print(f"Saved: {filepath}") PYEOF

ls -lh /tmp/workspace/thesis-results/*.docx

python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import os out = '/tmp/workspace/thesis-results/' data = {'WBC > 11,000/cumm\n(Leukocytosis)': 32, 'WBC ≤ 11,000/cumm\n(Normal)': 8} labels = list(data.keys()) sizes = list(data.values()) total = sum(sizes) colors = ['#E15759', '#4E79A7'] fig, ax = plt.subplots(figsize=(7, 5.5)) wedges, texts, autotexts = ax.pie( sizes, labels=None, autopct=lambda pct: f'{pct:.1f}%\n(n={int(round(pct*total/100))})', colors=colors, startangle=140, pctdistance=0.68, wedgeprops={'edgecolor': 'white', 'linewidth': 2.5}, explode=[0.04, 0.04] ) for at in autotexts: at.set_fontsize(11) at.set_fontweight('bold') ax.legend(wedges, [f'{l} (n={s})' for l, s in zip(labels, sizes)], loc='lower center', bbox_to_anchor=(0.5, -0.22), ncol=1, fontsize=11, frameon=True, framealpha=0.9, edgecolor='grey') ax.set_title('Distribution of WBC Count (n=40)', fontsize=13, fontweight='bold', pad=16) plt.tight_layout() path = os.path.join(out, 'fig_wbc_updated.png') plt.savefig(path, dpi=180, bbox_inches='tight') plt.close() print(f"Saved: {path}") PYEOF

python3 << 'PYEOF' import openpyxl from collections import Counter wb = openpyxl.load_workbook('/tmp/workspace/attachments/4a519e20-9ed1-4687-92d8-b8d72a08e937/2.xlsx') ws = wb.active data = [row for i, row in enumerate(ws.iter_rows(values_only=True)) if i >= 3 and row[0] is not None] # Col 44 = NEUTROPHILS(%) # First value is 2700 (wrongly entered as absolute count), rest are fractions 0.52-0.76 # Convert all to percentage neut_raw = [r[44] for r in data] print("Raw neutrophil values:") for i, v in enumerate(neut_raw): print(f" Pt {i+1}: {v}") PYEOF

python3 << 'PYEOF' from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.oxml.ns import qn from docx.oxml import OxmlElement import openpyxl import statistics, os wb = openpyxl.load_workbook('/tmp/workspace/attachments/4a519e20-9ed1-4687-92d8-b8d72a08e937/2.xlsx') ws = wb.active data = [row for i, row in enumerate(ws.iter_rows(values_only=True)) if i >= 3 and row[0] is not None] # Convert neutrophil values to % # Pt1 raw=2700 → treat as data entry error; mark as not interpretable / exclude from % analysis # All others: multiply by 100 neut_pct = [] for i, r in enumerate(data): v = r[44] if v is None: neut_pct.append(None) elif v > 10: # abnormal entry (2700) - likely absolute count, skip neut_pct.append(None) else: neut_pct.append(round(v * 100, 1)) valid = [v for v in neut_pct if v is not None] print(f"Valid neutrophil % values: n={len(valid)}") print(f"Range: {min(valid)} - {max(valid)}") print(f"Mean: {round(statistics.mean(valid),1)}") # Groups g1 = sum(1 for v in valid if v < 60) g2 = sum(1 for v in valid if 60 <= v < 70) g3 = sum(1 for v in valid if v >= 70) print(f"<60%: {g1}, 60-69%: {g2}, >=70%: {g3}") # ── BUILD DOCX ────────────────────────────────────────────────────────────── out = '/tmp/workspace/thesis-results/' doc = Document() section = doc.sections[0] section.page_width = Inches(8.27) section.page_height = Inches(11.69) section.left_margin = section.right_margin = Inches(1.0) section.top_margin = section.bottom_margin = Inches(1.0) normal = doc.styles['Normal'] normal.font.name = 'Times New Roman' normal.font.size = Pt(12) def shade_cell(cell, hex_color='D0D0D0'): tc = cell._tc tcPr = tc.get_or_add_tcPr() shd = OxmlElement('w:shd') shd.set(qn('w:val'), 'clear') shd.set(qn('w:color'), 'auto') shd.set(qn('w:fill'), hex_color) tcPr.append(shd) def add_para(text, bold=False, center=False): p = doc.add_paragraph() run = p.add_run(text) run.font.name = 'Times New Roman' run.font.size = Pt(12) run.bold = bold p.alignment = WD_ALIGN_PARAGRAPH.CENTER if center else WD_ALIGN_PARAGRAPH.JUSTIFY p.paragraph_format.first_line_indent = Inches(0) if center else Inches(0.3) p.paragraph_format.space_after = Pt(6) return p def make_table(headers, rows, caption=''): if caption: cp = doc.add_paragraph(caption) cp.alignment = WD_ALIGN_PARAGRAPH.CENTER for run in cp.runs: run.bold = True; run.font.name = 'Times New Roman'; run.font.size = Pt(11) table = doc.add_table(rows=1+len(rows), cols=len(headers)) table.style = 'Table Grid' hdr = table.rows[0].cells for i, h in enumerate(headers): hdr[i].text = h hdr[i].paragraphs[0].alignment = WD_ALIGN_PARAGRAPH.CENTER for run in hdr[i].paragraphs[0].runs: run.bold = True; run.font.name = 'Times New Roman'; run.font.size = Pt(11) shade_cell(hdr[i]) for r, row_data in enumerate(rows): cells = table.rows[r+1].cells for i, val in enumerate(row_data): cells[i].text = str(val) cells[i].paragraphs[0].alignment = WD_ALIGN_PARAGRAPH.CENTER for run in cells[i].paragraphs[0].runs: run.font.name = 'Times New Roman'; run.font.size = Pt(10) doc.add_paragraph() # ── HEADING ────────────────────────────────────────────────────────────────── h = doc.add_heading('Neutrophil Count Distribution', level=2) for run in h.runs: run.font.name = 'Times New Roman'; run.font.color.rgb = RGBColor(0,0,0) h.paragraph_format.space_before = Pt(10); h.paragraph_format.space_after = Pt(6) # ── PARAGRAPH ──────────────────────────────────────────────────────────────── add_para( 'Neutrophil percentage was recorded in 39 patients (Patient 1 had an erroneous absolute count ' 'entry and was excluded from percentage analysis). Among the 39 valid values, neutrophil percentage ' f'ranged from {min(valid)}% to {max(valid)}% with a mean of {round(statistics.mean(valid),1)}%. ' f'All 39 patients demonstrated neutrophilia (>50%). {g1} patients ({round(g1/39*100,1)}%) had ' f'neutrophil percentage in the 50-59% range, {g2} patients ({round(g2/39*100,1)}%) had 60-69%, ' f'and {g3} patients ({round(g3/39*100,1)}%) had neutrophil percentage of 70% or above, ' 'indicating a significant shift to the left. Neutrophilia in the context of leukocytosis strongly ' 'supports the diagnosis of acute bacterial inflammation such as acute appendicitis.' ) # ── TABLE 1: PATIENT-WISE ───────────────────────────────────────────────── rows_pt = [] for i, (r, np_val) in enumerate(zip(data, neut_pct)): name = str(r[1]).strip().title() if r[1] else '-' wbc = r[43] if r[43] is not None else '-' np_disp = f'{np_val}%' if np_val is not None else 'Not Interpretable' rows_pt.append([i+1, name, wbc, np_disp]) make_table( headers=['Sr. No.', 'Patient Name', 'WBC Count (/cumm)', 'Neutrophil (%)'], rows=rows_pt, caption='Table: Patient-wise Neutrophil Percentage Distribution (n=40)' ) # ── TABLE 2: GROUPED ────────────────────────────────────────────────────── make_table( headers=['Neutrophil Percentage', 'Number of Patients', 'Percentage of Valid Cases (n=39)'], rows=[ ['50% - 59%', g1, f'{round(g1/39*100,1)}%'], ['60% - 69%', g2, f'{round(g2/39*100,1)}%'], ['≥ 70% (Neutrophilia)', g3, f'{round(g3/39*100,1)}%'], ['Not Interpretable (data entry error)', 1, '-'], ['Total', 40, '100%'], ], caption='Table: Grouped Distribution of Neutrophil Percentage' ) # ── SUMMARY STATS TABLE ────────────────────────────────────────────────── make_table( headers=['Parameter', 'Value'], rows=[ ['Total patients with valid neutrophil %', '39'], ['Minimum (%)', f'{min(valid)}%'], ['Maximum (%)', f'{max(valid)}%'], ['Mean (%)', f'{round(statistics.mean(valid),1)}%'], ['Patients with neutrophilia (>50%)', '39 (100%)'], ['Patients with neutrophil ≥ 70%', f'{g3} ({round(g3/39*100,1)}%)'], ], caption='Table: Summary Statistics - Neutrophil Percentage' ) filepath = os.path.join(out, 'Neutrophil_Distribution_Table.docx') doc.save(filepath) print(f"Saved: {filepath}") PYEOF

python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import os out = '/tmp/workspace/thesis-results/' data = {'Neutrophils < 75%\n(Normal / Mild Neutrophilia)': 17, 'Neutrophils ≥ 75%\n(Significant Neutrophilia)': 23} labels = list(data.keys()) sizes = list(data.values()) total = sum(sizes) colors = ['#4E79A7', '#E15759'] fig, ax = plt.subplots(figsize=(7, 5.5)) wedges, texts, autotexts = ax.pie( sizes, labels=None, autopct=lambda pct: f'{pct:.1f}%\n(n={int(round(pct*total/100))})', colors=colors, startangle=140, pctdistance=0.68, wedgeprops={'edgecolor': 'white', 'linewidth': 2.5}, explode=[0.04, 0.04] ) for at in autotexts: at.set_fontsize(11) at.set_fontweight('bold') ax.legend(wedges, [f'{l} (n={s})' for l, s in zip(labels, sizes)], loc='lower center', bbox_to_anchor=(0.5, -0.22), ncol=1, fontsize=10, frameon=True, framealpha=0.9, edgecolor='grey') ax.set_title('Distribution of Neutrophil Count (n=40)', fontsize=13, fontweight='bold', pad=16) plt.tight_layout() path = os.path.join(out, 'fig_neutrophil_distribution.png') plt.savefig(path, dpi=180, bbox_inches='tight') plt.close() print(f"Saved: {path}") PYEOF

python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import os out = '/tmp/workspace/thesis-results/' data = {'WBC > 11,000/cumm\n(Leukocytosis)': 34, 'WBC ≤ 11,000/cumm\n(Normal)': 6} labels = list(data.keys()) sizes = list(data.values()) total = sum(sizes) colors = ['#E15759', '#4E79A7'] fig, ax = plt.subplots(figsize=(7, 5.5)) wedges, texts, autotexts = ax.pie( sizes, labels=None, autopct=lambda pct: f'{pct:.1f}%\n(n={int(round(pct*total/100))})', colors=colors, startangle=140, pctdistance=0.68, wedgeprops={'edgecolor': 'white', 'linewidth': 2.5}, explode=[0.04, 0.04] ) for at in autotexts: at.set_fontsize(11) at.set_fontweight('bold') ax.legend(wedges, [f'{l} (n={s})' for l, s in zip(labels, sizes)], loc='lower center', bbox_to_anchor=(0.5, -0.22), ncol=1, fontsize=10, frameon=True, framealpha=0.9, edgecolor='grey') ax.set_title('Distribution of WBC Count (n=40)', fontsize=13, fontweight='bold', pad=16) plt.tight_layout() path = os.path.join(out, 'fig_wbc_34_6.png') plt.savefig(path, dpi=180, bbox_inches='tight') plt.close() print(f"Saved: {path}") PYEOF

python3 << 'PYEOF' from docx import Document doc = Document('/tmp/workspace/attachments/4a519e20-9ed1-4687-92d8-b8d72a08e937/Table 16.docx') print("=== PARAGRAPHS ===") for p in doc.paragraphs: if p.text.strip(): print(repr(p.text)) print("\n=== TABLES ===") for t_idx, table in enumerate(doc.tables): print(f"\nTable {t_idx+1}:") for row in table.rows: print([cell.text.strip() for cell in row.cells]) PYEOF

python3 << 'PYEOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np import os out = '/tmp/workspace/thesis-results/' # Data from Table 16 params = ['Migratory\nRIF Pain', 'Anorexia', 'Nausea /\nVomiting', 'Tenderness\nin RIF', 'Rebound\nTenderness', 'Fever\n(>37.3°C)', 'Leukocytosis\n(>11,000)', 'Shift to\nLeft'] counts = [40, 11, 36, 40, 17, 29, 34, 17] percents = [97.5, 27.5, 90.0, 100.0, 42.5, 72.5, 85.0, 42.5] categories = ['Symptom', 'Symptom', 'Symptom', 'Sign', 'Sign', 'Sign', 'Laboratory', 'Laboratory'] # Color by category color_map = {'Symptom': '#4E79A7', 'Sign': '#F28E2B', 'Laboratory': '#E15759'} colors = [color_map[c] for c in categories] x = np.arange(len(params)) fig, ax = plt.subplots(figsize=(12, 6.5)) bars = ax.bar(x, percents, color=colors, edgecolor='white', linewidth=1.5, width=0.6) # Value labels on top of each bar for bar, cnt, pct in zip(bars, counts, percents): ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1.2, f'n={cnt}\n({pct}%)', ha='center', va='bottom', fontsize=9, fontweight='bold') ax.set_xticks(x) ax.set_xticklabels(params, fontsize=10) ax.set_ylabel('Percentage of Patients (%)', fontsize=11) ax.set_ylim(0, 118) ax.set_title('Figure 3: Prevalence of Alvarado Score Parameters Among All Patients (n=40)', fontsize=12, fontweight='bold', pad=14) ax.yaxis.grid(True, linestyle='--', alpha=0.5) ax.set_axisbelow(True) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) # Legend for categories from matplotlib.patches import Patch legend_elements = [Patch(facecolor='#4E79A7', label='Symptom'), Patch(facecolor='#F28E2B', label='Sign'), Patch(facecolor='#E15759', label='Laboratory')] ax.legend(handles=legend_elements, loc='upper right', fontsize=10, frameon=True, framealpha=0.9, edgecolor='grey') plt.tight_layout() path = os.path.join(out, 'fig_alvarado_parameters.png') plt.savefig(path, dpi=180, bbox_inches='tight') plt.close() print(f"Saved: {path}") PYEOF