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Cureus author guidelines original research manuscript format 2026

ophthalmology

mkdir -p "/tmp/workspace/ophthalmology-manuscript/src" "/tmp/workspace/ophthalmology-manuscript/output" && python3 -c "import pdfplumber; p='/tmp/workspace/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424/Abhishek ophthalmology final thesis datia.pdf'; out='/tmp/workspace/ophthalmology-manuscript/src/thesis_extracted.txt'; pdf=pdfplumber.open(p); print('pages',len(pdf.pages)); f=open(out,'w',encoding='utf-8'); [f.write('\\n\\n===== PAGE %d =====\\n'% (i+1) + (page.extract_text() or '')) for i,page in enumerate(pdf.pages)]; f.close()"

"CHAPTER" in ~/ophthalmology-manuscript/src

"RESULTS" in ~/ophthalmology-manuscript/src

"ABSTRACT" in ~/ophthalmology-manuscript/src

"CONCLUSION" in ~/ophthalmology-manuscript/src

https://www.cureus.com/author_guide/overview/step-by-step-ov…

"Table " in ~/ophthalmology-manuscript/src

"Ethical" in ~/ophthalmology-manuscript/src

neonatal eye screening red reflex examination universal newborn screening guidelines India ophthalmology

~/ophthalmology-manuscript

python3 - <<'PY' from docx import Document from docx.shared import Inches, Pt from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.section import WD_SECTION from docx.enum.table import WD_TABLE_ALIGNMENT, WD_CELL_VERTICAL_ALIGNMENT from docx.oxml import OxmlElement from docx.oxml.ns import qn from docx.enum.style import WD_STYLE_TYPE from docx.enum.text import WD_BREAK from pathlib import Path out=Path('/tmp/workspace/ophthalmology-manuscript/output') out.mkdir(parents=True,exist_ok=True) path=out/'Cureus_Original_Article_Universal_Newborn_Eye_Screening_Datia.docx' def shade(cell, fill): tcPr=cell._tc.get_or_add_tcPr(); shd=OxmlElement('w:shd'); shd.set(qn('w:fill'), fill); tcPr.append(shd) def set_cell_text(cell, text, bold=False): cell.text=''; p=cell.paragraphs[0]; r=p.add_run(str(text)); r.bold=bold; r.font.size=Pt(9) cell.vertical_alignment=WD_CELL_VERTICAL_ALIGNMENT.CENTER def add_table(doc, headers, rows, widths=None): t=doc.add_table(rows=1, cols=len(headers)); t.alignment=WD_TABLE_ALIGNMENT.CENTER; t.style='Table Grid' for i,h in enumerate(headers): set_cell_text(t.rows[0].cells[i],h,True); shade(t.rows[0].cells[i],'D9EAF7') for row in rows: cells=t.add_row().cells for i,x in enumerate(row): set_cell_text(cells[i],x) if widths: for row in t.rows: for i,w in enumerate(widths): row.cells[i].width=Inches(w) doc.add_paragraph() return t doc=Document() sec=doc.sections[0]; sec.top_margin=Inches(.7); sec.bottom_margin=Inches(.7); sec.left_margin=Inches(.8); sec.right_margin=Inches(.8) normal=doc.styles['Normal']; normal.font.name='Arial'; normal._element.rPr.rFonts.set(qn('w:eastAsia'),'Arial'); normal.font.size=Pt(10); normal.paragraph_format.space_after=Pt(5) for s in ['Title','Heading 1','Heading 2']: st=doc.styles[s]; st.font.name='Arial'; st._element.rPr.rFonts.set(qn('w:eastAsia'),'Arial'); st.font.color.rgb=None st.font.bold=True styles=doc.styles styles['Title'].font.size=Pt(18); styles['Heading 1'].font.size=Pt(13); styles['Heading 2'].font.size=Pt(11) # title page p=doc.add_paragraph(); p.alignment=WD_ALIGN_PARAGRAPH.CENTER r=p.add_run('ORIGINAL ARTICLE'); r.bold=True; r.font.size=Pt(11) p=doc.add_paragraph(); p.alignment=WD_ALIGN_PARAGRAPH.CENTER r=p.add_run('Universal Eye Screening in Newborns Delivered at a Tertiary Care Centre in the Bundelkhand Region, India: A Hospital-Based Cross-Sectional Study'); r.bold=True; r.font.size=Pt(17) p=doc.add_paragraph(); p.alignment=WD_ALIGN_PARAGRAPH.CENTER p.add_run('Dr Abhishek Rathod, MS Ophthalmology Resident¹; Dr Manoj Tyagi²; Dr M. S. Rajpoot³\n\n¹Department of Ophthalmology, Government Medical College, Datia, Madhya Pradesh, India\n²Professor and Head, Department of Ophthalmology, Government Medical College, Datia, Madhya Pradesh, India\n³Associate Professor, Department of Ophthalmology, Government Medical College, Datia, Madhya Pradesh, India').font.size=Pt(10) p=doc.add_paragraph(); p.alignment=WD_ALIGN_PARAGRAPH.CENTER p.add_run('Corresponding author: Dr Abhishek Rathod\nEmail: [insert institutional email] | ORCID: [insert if available]').italic=True doc.add_paragraph('Manuscript prepared for Cureus submission', style='Heading 2') doc.add_paragraph('Article type: Original Article. Word count: approximately 2,200 excluding abstract, references, and tables. Tables: 4. Figures: 0.') warning=doc.add_paragraph(); warning.style='Heading 2'; warning.add_run('Required author verification before submission') doc.add_paragraph('This manuscript was drafted from the supplied thesis. Confirm and complete every bracketed field, all author names/order/affiliations, institutional ethics approval number and date, consent language, funding and conflict disclosures, and the accuracy of the extracted data. Do not submit until the data inconsistencies listed in the editorial note have been reconciled against the source dataset.', style=None) doc.add_page_break() # abstract doc.add_heading('Abstract', level=1) for lab,text in [ ('Background','Early detection of ocular abnormalities may enable timely referral and treatment, but data from central India are limited. We evaluated the frequency and spectrum of ocular findings detected by universal newborn eye screening at a tertiary care centre in the Bundelkhand region.'), ('Methods','This hospital-based cross-sectional study enrolled consecutive live-born neonates examined within 48 hours of birth at Government Medical College, Datia, Madhya Pradesh, India, between October 2024 and December 2025. Screening comprised external examination, red reflex assessment, and indirect ophthalmoscopy. Demographic and perinatal characteristics were recorded. Categorical variables are presented as number (percentage); associations were evaluated using chi-square tests.'), ('Results','Among 1,100 screened neonates, 583 (53.0%) were male, 110 (10.0%) were preterm, and mean birth weight was 2,325.8 ± 716.2 g. At least one ocular anomaly was recorded in 86 neonates (7.82%). Subconjunctival hemorrhage was the most frequent finding (48, 4.36%), followed by iris coloboma (6, 0.54%). Temporal retinal immaturity (88, 8.0%) and retinal hemorrhage (55, 5.0%) were the most common documented fundus findings. Ocular anomalies were more frequent among low-birth-weight than normal-birth-weight neonates (62/165 [37.6%] vs 24/935 [2.6%], chi-square=206.82; p<0.001) and differed by gestational-age group (p<0.001). There was no association with sex (p=0.51).'), ('Conclusions','A structured examination within 48 hours of birth detected a range of anterior- and posterior-segment findings, including uncommon structural abnormalities. The findings support integration of a standardized newborn eye examination and referral pathway into postnatal services, while larger multicentre studies with longitudinal follow-up are needed to establish clinical outcomes and program cost-effectiveness.')]: p=doc.add_paragraph(); p.add_run(lab+': ').bold=True; p.add_run(text) p=doc.add_paragraph(); p.add_run('Keywords: ').bold=True; p.add_run('newborn; neonatal eye screening; red reflex; ocular abnormalities; congenital eye disease; India; retinal hemorrhage') # Intro doc.add_heading('Introduction', level=1) doc.add_paragraph('Childhood visual impairment can arise from congenital anomalies and treatable disorders that are identifiable early in life. The newborn examination provides an opportunity to identify anterior-segment abnormalities, leukocoria, media opacity, and selected posterior-segment findings. Red reflex assessment is a non-invasive component of neonatal screening and an abnormal result warrants prompt ophthalmic assessment.¹') doc.add_paragraph('A systematic review of universal newborn eye screening reported that programmes detect a broad spectrum of ocular abnormalities, although yield and case mix differ substantially with examination method, timing, population, and use of retinal imaging.² Evidence from India is particularly relevant because screening and referral capacity vary across settings. This study estimated the frequency and spectrum of ocular abnormalities detected through universal examination of newborns delivered at a tertiary care centre in the Bundelkhand region and explored associations with selected neonatal characteristics.') # Methods doc.add_heading('Materials and Methods', level=1) doc.add_heading('Study design and setting', level=2) doc.add_paragraph('This hospital-based cross-sectional observational study was conducted in the maternity wards and neonatal intensive care unit, in collaboration with the Departments of Obstetrics and Gynaecology, Paediatrics, and Ophthalmology at Government Medical College, Datia, Madhya Pradesh, India. Data collection occurred from October 2024 through December 2025.') doc.add_heading('Participants', level=2) doc.add_paragraph('All live newborns delivered at the study centre who underwent examination within 48 hours of birth were eligible. Newborns were excluded if parental or guardian consent was not provided or if clinical instability made ocular examination infeasible or medically contraindicated. Consecutive enrolment was used. A total of 1,100 neonates were examined.') doc.add_heading('Screening procedure and variables', level=2) doc.add_paragraph('Screening included external examination of the eyelids, conjunctiva, sclera, cornea, iris, and pupils; assessment for leukocoria; red reflex examination with direct ophthalmoscopy, including binocular red reflex assessment; and indirect ophthalmoscopy for fundus evaluation. Demographic and perinatal variables included sex, gestational age, birth weight, mode of delivery, and selected neonatal and maternal risk factors. The primary outcome was the proportion of screened newborns with at least one ocular anomaly recorded on the study proforma. Fundus findings were documented separately and may coexist with anterior-segment findings.') doc.add_heading('Statistical analysis', level=2) doc.add_paragraph('Data were entered in Microsoft Excel and analyzed in SPSS version 29.0. Continuous variables are reported as mean ± standard deviation and categorical variables as frequency and percentage. Associations between categorical variables and recorded ocular anomalies were assessed using chi-square tests. Two-sided p values below 0.05 were considered statistically significant. No multivariable model is reported because the supplied thesis did not provide adjusted estimates.') doc.add_heading('Ethics', level=2) p=doc.add_paragraph(); p.add_run('IMPORTANT - complete before submission: ').bold=True; p.add_run('The thesis states that institutional ethics approval was obtained before study initiation and that written informed consent was obtained from parents or legal guardians. Insert the exact approving committee name, approval number, and approval date here: [IEC NAME / APPROVAL NO. / DATE]. Confirm that consent covered research participation and, if applicable, publication of de-identified clinical images. No identifiable patient information is included in this manuscript.') # Results doc.add_heading('Results', level=1) doc.add_heading('Participant characteristics', level=2) doc.add_paragraph('Of 1,100 newborns screened, 583 (53.0%) were male and 517 (47.0%) were female. Most were term (942, 85.6%); 110 (10.0%) were preterm and 48 (4.4%) post-term. Mean birth weight was 2,325.8 ± 716.2 g (range, 970-4,050 g). Normal vaginal delivery was recorded in 862 neonates (78.4%), lower-segment caesarean section in 195 (17.7%), and ventouse-assisted delivery in 42 (3.8%) (Table 1).') add_table(doc,['Characteristic','Category','n (%)'],[ ['Sex','Male','583 (53.0)'],['','Female','517 (47.0)'],['Gestational age','Preterm','110 (10.0)'],['','Term','942 (85.6)'],['','Post-term','48 (4.4)'],['Mode of delivery','Normal vaginal delivery','862 (78.4)'],['','Lower-segment caesarean section','195 (17.7)'],['','Ventouse-assisted','42 (3.8)'],['Birth weight, g','Mean ± SD','2,325.8 ± 716.2'],['','Range','970-4,050']], [1.8,2.9,1.4]) doc.paragraphs[-1].insert_paragraph_before('Table 1. Baseline characteristics of screened newborns (N=1,100).').runs[0].bold=True doc.add_heading('Ocular and fundus findings', level=2) doc.add_paragraph('At least one ocular anomaly was recorded in 86 newborns (7.82%). The most frequent recorded anomaly was subconjunctival hemorrhage (48, 4.36%), followed by iris coloboma (6, 0.54%). Other findings are summarized in Table 2. The distribution represents recorded ocular anomalies and should not be interpreted as mutually exclusive with fundus findings.') add_table(doc,['Recorded ocular anomaly','n','% of 1,100'],[ ['Subconjunctival hemorrhage','48','4.36'],['Iris coloboma','6','0.54'],['Epicanthal fold','4','0.36'],['Yellow scleral discoloration','4','0.36'],['Corneal opacity','4','0.36'],['Limbal dermoid cyst','3','0.27'],['Persistent pupillary membrane','3','0.27'],['Congenital cataract','3','0.27'],['Congenital entropion','2','0.18'],['Microcornea/megalocornea','2','0.18'],['Anophthalmos, microphthalmos, buphthalmos, congenital glaucoma, or cleft lip/palate with ocular association','1 each','0.09 each'],['Any recorded ocular anomaly','86','7.82']], [3.8,.7,1.3]) doc.paragraphs[-1].insert_paragraph_before('Table 2. Recorded ocular anomalies detected during newborn screening.').runs[0].bold=True doc.add_paragraph('Fundus examination was documented as normal in 935 newborns (85.0%). Temporal retinal immaturity was documented in 88 (8.0%) and retinal hemorrhage in 55 (5.0%). Retinal or choroidal coloboma was recorded in five (0.5%) newborns. Fourteen examinations (1.2%) were recorded as not visualized (Table 3). Because categories in the source table are not clearly mutually exclusive, these values are reported as recorded findings rather than as a single prevalence estimate.') add_table(doc,['Fundus finding','n','% of 1,100'],[['Normal fundus','935','85.0'],['Temporal retinal immaturity','88','8.0'],['Retinal hemorrhage','55','5.0'],['Retinal/choroidal coloboma','5','0.5'],['Persistent hyperplastic primary vitreous (PHPV)','3','0.3'],['Fundus not visualized','14','1.2'],['Persistent fetal vasculature (PFV/PHPV)','1','0.1'],['Retinal hamartoma','1','0.1']], [3.8,.7,1.3]) doc.paragraphs[-1].insert_paragraph_before('Table 3. Fundus findings recorded during universal newborn eye screening.').runs[0].bold=True doc.add_heading('Association with neonatal characteristics', level=2) doc.add_paragraph('Recorded ocular anomalies were present in 62 of 165 low-birth-weight newborns (37.6%) and 24 of 935 newborns with birth weight 2,500 g or greater (2.6%; chi-square=206.82, 1 degree of freedom; p<0.001). Frequencies also differed by gestational-age category (chi-square=93.13, 2 degrees of freedom; p<0.001). There was no statistically significant association with sex (p=0.51) (Table 4). These are unadjusted comparisons and should not be interpreted as independent causal effects.') add_table(doc,['Variable','Ocular anomaly present','Absent','Chi-square (df); p value'],[['Birth weight <2,500 g','62','103','206.82 (1); <0.001'],['Birth weight ≥2,500 g','24','911',''],['Preterm','39','71','93.13 (2); <0.001'],['Term','37','905',''],['Post-term','10','38',''],['Male','47','536','0.43 (1); 0.51'],['Female','39','478','']], [1.7,1.4,1.0,2.1]) doc.paragraphs[-1].insert_paragraph_before('Table 4. Unadjusted association of birth weight, gestational age, and sex with recorded ocular anomalies.').runs[0].bold=True # Discussion doc.add_heading('Discussion', level=1) doc.add_paragraph('In this hospital-based cross-sectional study, 7.82% of newborns had at least one recorded ocular anomaly on examination within 48 hours of birth. Subconjunctival hemorrhage was the most common anomaly, while the screening process also identified uncommon structural abnormalities, including corneal opacity, congenital cataract, glaucoma, and ocular coloboma. The results illustrate the potential value of a structured ocular examination in the immediate postnatal period, especially where routine examination may not include a dedicated ophthalmic assessment.') doc.add_paragraph('The observed frequency should be compared cautiously with other studies. A systematic review found substantial heterogeneity in study populations, examination timing, definitions, and technology used for universal newborn eye screening.² In a prospective study in New Zealand, universal newborn eye screening identified clinically relevant findings in both hospital and community settings.³ Retinal hemorrhage is commonly associated with birth-related mechanical factors and may resolve spontaneously, but its frequency varies with timing and method of examination.⁴') doc.add_paragraph('Low birth weight and preterm birth were associated with recorded ocular anomalies in the present dataset. These findings are clinically plausible, but the analyses are unadjusted and low birth weight, prematurity, respiratory illness, and neonatal intensive care exposure can be correlated. Therefore, they should be regarded as signals for careful clinical assessment rather than evidence of independent risk factors. The presence of potentially sight-threatening conditions reinforces the importance of a reliable referral pathway following an abnormal red reflex or structural finding.¹,⁵') doc.add_heading('Limitations', level=2) doc.add_paragraph('The study was conducted at a single tertiary centre with consecutive/convenience sampling and may not represent the general newborn population. Screening was limited to the first 48 hours and no long-term outcome data were available. Wide-field retinal imaging was not routinely used. The results are based on recorded clinical findings; some categories may overlap, and the supplied source tables contain internal inconsistencies that require dataset verification. Maternal and neonatal risk-factor analyses were unadjusted, so residual confounding is likely.') doc.add_heading('Conclusions', level=1) doc.add_paragraph('Universal newborn ocular examination at this tertiary centre detected a broad range of ocular findings, including occasional structural abnormalities requiring ophthalmic assessment. A standardized postnatal examination, red reflex testing, documentation, and referral pathway may help detect infants requiring timely specialist review. Future multicentre studies should use prespecified non-overlapping outcome definitions, confirmatory imaging where available, and longitudinal visual outcomes.') # declarations doc.add_heading('Declarations', level=1) for h,t in [ ('Human subjects','[Complete before submission] Institutional Ethics Committee approval: [name, approval number, date]. The authors confirm that written informed consent was obtained from parents or legal guardians for study participation.'), ('Animal subjects','All authors confirm that this study did not involve animal subjects or tissue.'), ('Conflicts of interest','[Complete before submission] The authors declare: [no conflicts of interest / specify all conflicts].'), ('Funding','[Complete before submission] [No external funding was received / specify funding source and role].'), ('Data availability','[Complete before submission] De-identified data may be made available by the corresponding author on reasonable request, subject to institutional ethics requirements. Confirm this statement with the institution before submission.'), ('Author contributions','[Complete before submission using CRediT roles.] Suggested verification only: Abhishek Rathod: conceptualization, data curation, investigation, formal analysis, writing - original draft. Manoj Tyagi: supervision, methodology, writing - review and editing. M. S. Rajpoot: supervision, methodology, writing - review and editing. All authors must approve the final version and meet ICMJE authorship criteria.'), ('Acknowledgments','The authors thank the Departments of Obstetrics and Gynaecology and Paediatrics, Government Medical College, Datia, and the participating newborns and their families. [Revise as appropriate.]')]: doc.add_heading(h,level=2); doc.add_paragraph(t) # refs doc.add_heading('References', level=1) refs=[ '1. Loh AR, Chiang MF. Pediatric vision screening. Pediatr Rev. 2018;39(5):225-234. doi:10.1542/pir.2017-0177.', '2. Malik ANJ, Evans JR, Gupta S, et al. Universal newborn eye screening: a systematic review of the literature and review of international guidelines. J Glob Health. 2022;12:12003. doi:10.7189/jogh.12.12003. PMID: 36269293.', '3. Simkin SK, Misra SL, Battin M, McGhee CNJ, Dai S. Prospective observational study of universal newborn eye screening in a hospital and community setting in New Zealand. BMJ Paediatr Open. 2019;3(1):e000376. doi:10.1136/bmjpo-2018-000376.', '4. Emerson MV, Pieramici DJ, Stoessel KM, Berreen JP, Gariano RF. Incidence and rate of disappearance of retinal hemorrhage in newborns. Ophthalmology. 2001;108(1):36-39. doi:10.1016/S0161-6420(00)00466-4.', '5. American Association for Pediatric Ophthalmology and Strabismus. Vision screening recommendations. Accessed October 12, 2026. https://aapos.org/careers/vision-screening-patients.' ] for x in refs: doc.add_paragraph(x,style=None) # editorial note sec2=doc.add_section(WD_SECTION.NEW_PAGE) doc.add_heading('Editorial and Data-Verification Note (Do Not Submit as Part of Manuscript)', level=1) doc.add_paragraph('This page is intentionally included to prevent avoidable desk rejection or statistical criticism. Delete it only after each item has been verified and addressed.') items=[ 'The thesis title describes the work as “universal eye screening,” but the design is cross-sectional and not an evaluation of diagnostic test accuracy, effectiveness, or cost-effectiveness. The manuscript uses “detected” rather than claiming effectiveness.', 'The supplied Table 4 fundus counts sum to more than 1,100 and lists PHPV/PFV in two rows. Categories likely overlap or contain a duplicate. Verify each record and use mutually exclusive categories or explicitly report coexisting findings.', 'The supplied Table 6 reports 62 low-birth-weight newborns with anomalies, whereas supplied Table 7 shows 40. Table 7 also gives risk-factor counts that may be overlapping. Do not submit Table 7 or any adjusted-effect claim until the raw dataset is reanalyzed.', 'The ocular-anomaly total of 86 matches the sum of Table 5 categories. However, confirm whether epicanthal folds, scleral discoloration, and subconjunctival hemorrhage were intended to be classified as “ocular anomalies” versus transient findings. Consider prespecifying primary outcome as “any ocular finding” and a secondary outcome of “potentially vision-threatening/structural anomaly.”', 'Confirm whether pupils were dilated before indirect ophthalmoscopy, who performed the examination, examiner training, interobserver quality control, referral outcomes, and whether all 1,100 infants had every component of the examination. Add these details if available.', 'Replace all bracketed declaration text. Upload ethics approval documentation if the journal requests it. Ensure every author meets ICMJE authorship criteria and approves the exact submitted version.', 'Run an institutional plagiarism/similarity check and rewrite any retained thesis language. A thesis should be disclosed if required by the chosen journal. Confirm journal scope, processing charges, indexing status, and current author instructions before submission.' ] for i,x in enumerate(items,1): doc.add_paragraph(f'{i}. {x}') doc.add_heading('Cureus submission checklist',level=2) for x in ['Finalized author order, affiliations, corresponding email, and ORCID','Exact IEC approval number/date and parent/guardian consent statement','Data reconciliation and updated tables/statistics from raw dataset','All disclosures, funding, and author-contribution fields completed','At least five potential advisers entered in Cureus submission system','Tables entered as editable tables, not images; any clinical images fully de-identified with appropriate consent','STROBE cross-sectional checklist completed and uploaded if requested']: doc.add_paragraph('☐ '+x) # footer page numbers for section in doc.sections: footer=section.footer.paragraphs[0]; footer.alignment=WD_ALIGN_PARAGRAPH.CENTER footer.add_run('Universal Newborn Eye Screening | Government Medical College, Datia | Page ') fld=OxmlElement('w:fldSimple'); fld.set(qn('w:instr'),'PAGE'); footer._p.append(fld) doc.save(path) print(path) PY python3 - <<'PY' from docx import Document p='/tmp/workspace/ophthalmology-manuscript/output/Cureus_Original_Article_Universal_Newborn_Eye_Screening_Datia.docx' d=Document(p) print('paragraphs',len(d.paragraphs),'tables',len(d.tables)) print('title:',d.paragraphs[1].text) print('size',__import__('os').path.getsize(p)) PY

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STROBE statement cross-sectional study reporting tables data consistency

python3 - <<'PY' import pdfplumber p='/tmp/workspace/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424/Abhishek ophthalmology final thesis datia.pdf' with pdfplumber.open(p) as pdf: for i in range(110,155): tx=pdf.pages[i].extract_text() or '' print(i+1, tx[:90].replace('\n',' | ')) PY

python3 - <<'PY' import pdfplumber, json p='/tmp/workspace/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424/Abhishek ophthalmology final thesis datia.pdf' with pdfplumber.open(p) as pdf: for ix in [127,128,154]: pg=pdf.pages[ix] print('\nPAGE',ix+1,pg.width,pg.height, 'rotation',pg.rotation) print('tables',len(pg.find_tables())) for ti,t in enumerate(pg.find_tables()): data=t.extract() print('table',ti,'dims',len(data),len(data[0]) if data else 0) for r in data[:5]: print(r) PY

python3 - <<'PY' import pdfplumber, pandas as pd, re, json, os src='/tmp/workspace/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424/Abhishek ophthalmology final thesis datia.pdf' out='/tmp/workspace/ophthalmology-manuscript/src' os.makedirs(out,exist_ok=True) rows=[] with pdfplumber.open(src) as pdf: for page_no in range(128,156): tabs=pdf.pages[page_no-1].find_tables() if not tabs: print('no table',page_no); continue data=tabs[0].extract() for r in data[1:]: if not r or not r[0] or not re.fullmatch(r'\d+',r[0].strip()): continue rows.append([('' if x is None else x.replace('\n',' ').strip()) for x in r]+[page_no]) cols=['serial','id','sex','weight','ga','delivery','date','pediatric_risk','maternal_risk','facial','eyelid','globe','discharge','conj_redness','cornea','iris_prolapse','pupil','sclera','cornea2','iris','pupil2','lens','fundus','congenital','pdf_page'] df=pd.DataFrame(rows,columns=cols) for c in ['serial','weight']: df[c]=pd.to_numeric(df[c],errors='coerce') df.to_csv(out+'/master_chart_extracted.csv',index=False) print('ROWS',len(df),'serial minmax',df.serial.min(),df.serial.max(),'unique serial',df.serial.nunique(),'duplicates',df.serial.duplicated().sum()) print('missing serials',set(range(1,1101))-set(df.serial.dropna().astype(int))) print(df[['serial','id','sex','weight','ga','delivery','pediatric_risk','maternal_risk','fundus','congenital']].tail(20).to_string(index=False)) print('\nSEX\n',df.sex.value_counts(dropna=False));print('\nDELIVERY\n',df.delivery.value_counts(dropna=False));print('\nPED\n',df.pediatric_risk.value_counts(dropna=False));print('\nMAT\n',df.maternal_risk.value_counts(dropna=False));print('\nFUNDUS\n',df.fundus.value_counts(dropna=False));print('\nCONGEN\n',df.congenital.value_counts(dropna=False)) PY

python3 - <<'PY' import pandas as pd p='/tmp/workspace/ophthalmology-manuscript/src/master_chart_extracted.csv'; d=pd.read_csv(p).fillna('') for c in d.columns: print('\n===',c,'===') print(d[c].value_counts().to_string()) print('\nBLANK CORE') print(d[(d.sex=='') | (d.delivery=='') | (d.pediatric_risk=='') | (d.maternal_risk=='')][['serial','id','sex','weight','ga','delivery','pediatric_risk','maternal_risk','facial','eyelid','globe','discharge','conj_redness','cornea','iris_prolapse','pupil','sclera','cornea2','iris','pupil2','lens','fundus','congenital']].to_string(index=False)) PY

python3 - <<'PY' import pandas as pd, re, numpy as np from scipy.stats import chi2_contingency p='/tmp/workspace/ophthalmology-manuscript/src/master_chart_extracted.csv'; d=pd.read_csv(p).fillna('') # Workable records omit essentially blank serial15 v=d[d.serial!=15].copy(); print('usable',len(v)) def ga_w(x): s=str(x).strip(); m=re.match(r'^(\d+)(?:\+(\d+))?$',s) return int(m.group(1))+(int(m.group(2) or 0)/7) if m else np.nan v['ga_weeks']=v.ga.map(ga_w) print('sex',v.sex.value_counts().to_dict()); print('delivery',v.delivery.value_counts().to_dict()); print('wt mean sd range',v.weight.mean(),v.weight.std(ddof=1),v.weight.min(),v.weight.max(),'LBW',sum(v.weight<2500),'normal',sum(v.weight>=2500),'missingwt',v.weight.isna().sum()) print('ga n/miss',v.ga_weeks.notna().sum(),v.ga_weeks.isna().sum(),'preterm',sum(v.ga_weeks<37),'term',sum((v.ga_weeks>=37)&(v.ga_weeks<42)),'post',sum(v.ga_weeks>=42)) # Fundus normal vs categories f=v.fundus.str.upper().str.replace(' ','',regex=False) classes={ 'normal': f.str.contains('NORMAL') & ~f.str.contains('HEMORRH'), 'immature': f.str.contains('IMMATURE'), 'hemorrhage': f.str.contains('HEMORRH|HEAMORR'), 'coloboma': f.str.contains('COLOBOMA'), 'not_visualized': f.str.contains('NOTVISUAL'), 'hamartoma': f.str.contains('HAMARTOMA'), 'other': f.str.contains('MULINATED|ABNORMAL') } for k,x in classes.items():print(k,x.sum(),v.loc[x,'serial'].tolist()) print('fundus blank',sum(v.fundus=='')) # ocular finding any: non-normal external, congenital labels; data cells specific external={ 'subconj hemorrhage':v.cornea.str.contains('subconjunctival',case=False,na=False), 'microcornea':v.sclera.str.contains('microcornea',case=False), 'megalocornea':v.sclera.str.contains('megalocorn',case=False), 'corneal opacity/hazy':v.sclera.str.contains('cornea hazy',case=False), 'yellow sclera':v.cornea2.str.contains('yellow',case=False), } for k,x in external.items():print(k,x.sum(),v.loc[x,'serial'].tolist()) print('congenital values',v.congenital.value_counts().to_dict()) # has any anomaly used thesis: SCH OR non NONE congenital OR special sclera yellow/micro/mega/hazy ; exclude existing blank non_none=(v.congenital!='NONE')&(v.congenital!='') any_eye=external['subconj hemorrhage'] | non_none | external['microcornea'] | external['megalocornea'] | external['corneal opacity/hazy'] | external['yellow sclera'] print('any eye',any_eye.sum(), 'cong labels nonnone',non_none.sum(),'overlaps sch-cong',(external['subconj hemorrhage']&non_none).sum()) # raw ped / maternal counts labels incl weird normal print('ped nonnone',v.pediatric_risk.value_counts().to_dict());print('maternal',v.maternal_risk.value_counts().to_dict()) # cross table for raw outcome with LBW and GA for nm,grp in [('lbw',v.weight<2500),('preterm',v.ga_weeks<37),('sexmale',v.sex=='MALE')]: tab=pd.crosstab(grp,any_eye); print(nm,tab.to_dict(),chi2_contingency(tab,correction=False)) PY

cat > "/tmp/workspace/ophthalmology-manuscript/src/audit_analysis.py" <<'PY' import pandas as pd, numpy as np, re from scipy.stats import chi2_contingency from datetime import datetime p='/tmp/workspace/ophthalmology-manuscript/src/master_chart_extracted.csv' d=pd.read_csv(p).fillna('') d['valid_record']=d.serial!=15 v=d[d.valid_record].copy() def ga_w(x): m=re.match(r'^(\d+)(?:\+(\d+))?$',str(x).strip()) return int(m.group(1))+int(m.group(2) or 0)/7 if m else np.nan v['ga_weeks']=v.ga.map(ga_w) v['ga_group']=np.select([v.ga_weeks<37,(v.ga_weeks>=37)&(v.ga_weeks<42),v.ga_weeks>=42],['Preterm','Term','Post-term'],default='Missing/invalid') v['bw_group']=np.where(v.weight<2500,'Low birth weight (<2500 g)','Birth weight ≥2500 g') # normalize anterior/structural categories based exact raw chart fields v['subconjunctival_hemorrhage']=v.cornea.str.contains('subconjunctival',case=False,na=False) v['yellow_sclera']=v.cornea2.str.contains('yellow',case=False,na=False) v['microcornea']=v.sclera.str.contains('microcornea',case=False,na=False) v['megalocornea']=v.sclera.str.contains('megalocorn',case=False,na=False) v['corneal_haze']=v.sclera.str.contains('cornea hazy',case=False,na=False) cong=v.congenital.str.upper().str.strip() # all congenital labels as row-level condition. record has only one field, but varies formatting v['iris_coloboma']=cong.str.contains('IRIS COLOBOMA') v['cong_corneal_opacity']=cong.str.contains('CORNEAL OPACITY') v['phpv']=cong.str.contains('HYPERPLASTIC') v['limbal_dermoid']=cong.str.contains('LIMBAL DERMOID') v['cong_cataract']=cong.str.contains('CONGENITAL CATARACT') v['ppm']=cong.str.contains('PERSISTENT PUPILLARY') v['epicanthal_fold']=cong.str.contains('EPICANTHAL') v['cong_entropion']=cong.str.contains('ENTROPION') v['cong_ptosis']=cong.str.contains('PTOSIS') v['albinic_fundus']=cong.str.contains('ALBINIC') v['pfv']=cong.eq('PFV') v['blue_sclera']=cong.str.contains('BLUE SCLERA') v['microphthalmos']=cong.str.contains('MICROOPHTHALMOS|MICROPHTHALMOS') v['buphthalmos']=cong.str.contains('BUPHTHALMOS') v['anophthalmos']=cong.str.contains('ANOPHTHALMOS') v['retinoblastoma']=cong.str.contains('RETINOBLATOMA') # fundus normalized; temporal only (typos), excluding n/a f=v.fundus.str.upper().str.replace(' ','',regex=False) v['fundus_normal']=f.str.contains('NORMAL') & ~f.str.contains('HEMORRH') v['retinal_immaturity']=f.str.contains('IMMATURE') v['retinal_hemorrhage']=f.str.contains('HEMORRH|HEAMORR') v['retinal_choroidal_coloboma']=f.str.contains('COLOBOMA') v['fundus_not_visualized']=f.str.contains('NOTVISUAL') v['retinal_hamartoma']=f.str.contains('HAMARTOMA') v['myelinated_nerve_fibers']=f.str.contains('MULINATED') v['other_unspecified_fundus']=f.eq('ABNORMAL') # Any recorded finding (exclude generic abnormal condition of visual acuity? no) finding_cols=['subconjunctival_hemorrhage','yellow_sclera','microcornea','megalocornea','corneal_haze','iris_coloboma','cong_corneal_opacity','phpv','limbal_dermoid','cong_cataract','ppm','epicanthal_fold','cong_entropion','cong_ptosis','albinic_fundus','pfv','blue_sclera','microphthalmos','buphthalmos','anophthalmos','retinoblastoma','retinal_immaturity','retinal_hemorrhage','retinal_choroidal_coloboma','retinal_hamartoma','myelinated_nerve_fibers','other_unspecified_fundus'] v['any_recorded_ocular_finding']=v[finding_cols].any(axis=1) v['anterior_or_structural_finding']=v[finding_cols[:21]].any(axis=1) # tables def tab(series, name='Category'): x=series.value_counts(dropna=False).rename_axis(name).reset_index(name='n');x['%']=x.n/len(v)*100; return x baseline=pd.DataFrame({'Characteristic':['Sex: male','Sex: female','Gestational age: preterm','Gestational age: term','Gestational age: post-term','Gestational age: missing/invalid','Delivery: vaginal','Delivery: LSCS','Delivery: ventouse','Birth weight, mean ± SD (g)','Birth weight, range (g)','Low birth weight (<2500 g)','Birth weight ≥2500 g'], 'n_or_summary':[583,516,570,522,6,1,862,195,42,f'{v.weight.mean():.1f} ± {v.weight.std(ddof=1):.1f}',f'{int(v.weight.min())}-{int(v.weight.max())}',int((v.weight<2500).sum()),int((v.weight>=2500).sum())], '%':[53.05,46.95,51.91,47.50,.55,.09,78.44,17.74,3.82,'','',60.87,39.13]}) struct_items=[('Subconjunctival hemorrhage','subconjunctival_hemorrhage'),('Yellow scleral discoloration','yellow_sclera'),('Microcornea','microcornea'),('Megalocornea','megalocornea'),('Corneal haze','corneal_haze'),('Iris coloboma','iris_coloboma'),('Congenital corneal opacity','cong_corneal_opacity'),('Persistent hyperplastic primary vitreous','phpv'),('Persistent fetal vasculature','pfv'),('Limbal dermoid','limbal_dermoid'),('Congenital cataract','cong_cataract'),('Persistent pupillary membrane','ppm'),('Epicanthal fold','epicanthal_fold'),('Congenital entropion','cong_entropion'),('Congenital ptosis','cong_ptosis'),('Albinic fundus','albinic_fundus'),('Blue sclera','blue_sclera'),('Microphthalmos','microphthalmos'),('Buphthalmos','buphthalmos'),('Anophthalmos','anophthalmos'),('Retinoblastoma (spelling as recorded)','retinoblastoma')] struct=pd.DataFrame([(a,int(v[b].sum()),round(v[b].mean()*100,2)) for a,b in struct_items],columns=['Finding','n','%']) struct=struct[struct.n>0] fund_items=[('Normal fundus recorded','fundus_normal'),('Retinal immaturity','retinal_immaturity'),('Retinal hemorrhage','retinal_hemorrhage'),('Retinal/choroidal coloboma','retinal_choroidal_coloboma'),('Fundus not visualized','fundus_not_visualized'),('Retinal hamartoma','retinal_hamartoma'),('Myelinated nerve fibers','myelinated_nerve_fibers'),('Other unspecified fundus abnormality','other_unspecified_fundus')] fund=pd.DataFrame([(a,int(v[b].sum()),round(v[b].mean()*100,2)) for a,b in fund_items],columns=['Fundus finding','n','%']) # mutually exclusive / raw fundus table categories can overlap normal and etc? calculate records with >1 fund category fundcols=[b for a,b in fund_items] v['fundus_category_count']=v[fundcols].sum(axis=1) # associations only exploratory. no claim effect assoc=[] for var,cats in [('Birth weight category',['Low birth weight (<2500 g)','Birth weight ≥2500 g']),('Gestational age category',['Preterm','Term','Post-term']),('Sex',['MALE','FEMALE'])]: ser=v['bw_group'] if var.startswith('Birth') else (v['ga_group'] if var.startswith('Gest') else v.sex) vv=v[ser.isin(cats)] ct=pd.crosstab(ser[ser.isin(cats)],vv.any_recorded_ocular_finding) chi,p,df,ex=chi2_contingency(ct,correction=False) for cat in cats: a=int(ct.loc[cat,True]); n=int(ct.loc[cat].sum()); assoc.append((var,cat,a,n-a,n,round(a/n*100,2),round(chi,2),df,round(p,4))) assoc=pd.DataFrame(assoc,columns=['Variable','Category','Finding present','Finding absent','Total','% with finding','Chi-square','df','p value']) # dates dates=pd.to_datetime(v.date,format='%d-%m-%Y',errors='coerce') period_start=pd.Timestamp('2024-10-01');period_end=pd.Timestamp('2025-12-31') dateaudit=pd.DataFrame({'Metric':['Valid records','Blank/corrupted record excluded','Parseable date','Unparseable/missing date','Dates before 01-Oct-2024','Dates after 31-Dec-2025','Date range among parseable dates'], 'Value':[len(v),len(d)-len(v),dates.notna().sum(),dates.isna().sum(),(dates<period_start).sum(),(dates>period_end).sum(),f'{dates.min().date()} to {dates.max().date()}']}) # retained issues issues=pd.DataFrame([ ['Denominator','Tables 1-7 state N=1,100, but master chart has 1,100 serial numbers with serial 15 completely blank. All analyzable variables sum to 1,099.','Use N=1,099 for analyses unless serial 15 can be recovered from original case records.'], ['Sex','Master chart: 583 male + 516 female = 1,099. Thesis Table 1 reports 517 females and N=1,100.','Correct female count to 516 (46.95%).'], ['Gestational age','Master chart yields 570 preterm, 522 term, 6 post-term, 1 invalid/missing, not 110/942/48.','Replace table with chart-derived values; confirm definitions and correct the one invalid entry.'], ['Birth weight','Master chart mean 2325.8±716.2 g agrees with thesis, but 669/1,099 (60.87%) weigh <2500 g, not 165/1100 (15.0%).','Correct LBW count or verify if the master-chart weight field is not birth weight.'], ['Pediatric risk factor','The “pediatric risk” field contains LBW 231, LBW+respiratory distress 13, respiratory distress 6, 76 “NORMAL,” 769 “NONE,” and 2 blank. It does not support thesis counts for LBW 165, RDS 32, HIE 24, sepsis 17.','Do not publish Table 2 or Table 7 until source case records are recoded.'], ['Maternal risk factor','Master chart has PIH 214 and GDM 187, but obstructed labour is 197, not 123; maternal anemia and prolonged labour are absent; 2 blanks.','Correct only values supported by chart; do not infer missing variables.'], ['Fundus table','Thesis Table 4 categories total 1,102, not 1,100, and PHPV/PFV are duplicated. Master chart yields 87 immaturity, 60 hemorrhage, 5 coloboma, 16 not visualized, 1 hamartoma, but categories may coexist.','Report as non-mutually-exclusive recorded findings with denominator 1,099; remove total row.'], ['Congenital/anterior table','Thesis Table 5 sums to 86 but master chart contains 48 SCH and 37 non-NONE congenital labels plus other findings; records can overlap. “Congenital” includes birth-related SCH and yellow sclera.','Rename to “Recorded anterior-segment/external and structural findings”; do not total individual rows.'], ['Association tables','Original Table 6 uses internally inconsistent margins. With master-chart data and a corrected composite finding, associations are materially different.','Withdraw Tables 6-7 and reanalyze only after cleaning definitions and variables.'], ['Study dates','Methods state Oct 2024-Dec 2025. Master chart contains 728 dates before 01-Oct-2024 and 2 dates after 31-Dec-2025, including dates in 2024/2026.','Verify dates against hospital records and revise study-period description or exclude ineligible cases prospectively.'] ],columns=['Domain','Audit finding','Required correction']) # save with pd.ExcelWriter('/tmp/workspace/ophthalmology-manuscript/output/Audited_Thesis_Data_Tables.xlsx',engine='openpyxl') as w: d.to_excel(w,index=False,sheet_name='Extracted_Raw_Chart') v.to_excel(w,index=False,sheet_name='Cleaned_Chart') baseline.to_excel(w,index=False,sheet_name='Corrected_Table_1') struct.to_excel(w,index=False,sheet_name='Corrected_Table_2') fund.to_excel(w,index=False,sheet_name='Corrected_Table_3') assoc.to_excel(w,index=False,sheet_name='Exploratory_Associations') dateaudit.to_excel(w,index=False,sheet_name='Date_Audit') issues.to_excel(w,index=False,sheet_name='Audit_Issues') # outputs for doc generator for nm,x in [('baseline',baseline),('struct',struct),('fund',fund),('assoc',assoc),('issues',issues),('dateaudit',dateaudit)]:x.to_csv(f'/tmp/workspace/ophthalmology-manuscript/src/{nm}.csv',index=False) print('SUMMARY') print(baseline.to_string(index=False));print(struct.to_string(index=False));print(fund.to_string(index=False));print(assoc.to_string(index=False));print(dateaudit.to_string(index=False));print('fund category>1',sum(v.fundus_category_count>1),'fund none',sum(v.fundus_category_count==0));print('any anterior/struct',v.anterior_or_structural_finding.sum(),'any all ocular',v.any_recorded_ocular_finding.sum()) PY python3 "/tmp/workspace/ophthalmology-manuscript/src/audit_analysis.py" python3 - <<'PY' from openpyxl import load_workbook from openpyxl.styles import Font, PatternFill, Alignment p='/tmp/workspace/ophthalmology-manuscript/output/Audited_Thesis_Data_Tables.xlsx'; wb=load_workbook(p) for ws in wb.worksheets: ws.freeze_panes='A2';ws.auto_filter.ref=ws.dimensions for c in ws[1]: c.font=Font(bold=True,color='FFFFFF');c.fill=PatternFill('solid',fgColor='1F4E78');c.alignment=Alignment(wrap_text=True) for col in ws.columns: mx=max(len(str(x.value or '')) for x in col); ws.column_dimensions[col[0].column_letter].width=min(max(mx+2,11),45) for row in ws.iter_rows(min_row=2): for c in row: c.alignment=Alignment(vertical='top',wrap_text=True) wb.save(p) print('xlsx saved') PY

python3 - <<'PY' from docx import Document p='/tmp/workspace/ophthalmology-manuscript/output/Cureus_Original_Article_Universal_Newborn_Eye_Screening_Datia.docx' d=Document(p) print('tables',len(d.tables),'paras',len(d.paragraphs)) for i,p in enumerate(d.paragraphs[:20]): if p.text.strip():print(i,p.text[:160]) PY

~/ophthalmology-manuscript/output

cat > "/tmp/workspace/ophthalmology-manuscript/src/create_audit_docs.py" <<'PY' from docx import Document from docx.shared import Inches, Pt from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.table import WD_TABLE_ALIGNMENT, WD_CELL_VERTICAL_ALIGNMENT from docx.oxml import OxmlElement from docx.oxml.ns import qn from pathlib import Path import pandas as pd, shutil, os base=Path('/tmp/workspace/ophthalmology-manuscript') out=base/'output'; src=base/'src' baseline=pd.read_csv(src/'baseline.csv').fillna('') struct=pd.read_csv(src/'struct.csv').fillna('') fund=pd.read_csv(src/'fund.csv').fillna('') assoc=pd.read_csv(src/'assoc.csv').fillna('') issues=pd.read_csv(src/'issues.csv').fillna('') dateaudit=pd.read_csv(src/'dateaudit.csv').fillna('') def setup(doc): sec=doc.sections[0];sec.top_margin=Inches(.7);sec.bottom_margin=Inches(.7);sec.left_margin=Inches(.75);sec.right_margin=Inches(.75) n=doc.styles['Normal'];n.font.name='Arial';n._element.rPr.rFonts.set(qn('w:eastAsia'),'Arial');n.font.size=Pt(9);n.paragraph_format.space_after=Pt(4) for nm,sz in [('Title',17),('Heading 1',13),('Heading 2',11)]: st=doc.styles[nm];st.font.name='Arial';st._element.rPr.rFonts.set(qn('w:eastAsia'),'Arial');st.font.size=Pt(sz);st.font.bold=True def shade(cell,color): tcPr=cell._tc.get_or_add_tcPr();shd=OxmlElement('w:shd');shd.set(qn('w:fill'),color);tcPr.append(shd) def table(doc,df,widths=None): t=doc.add_table(rows=1,cols=len(df.columns));t.style='Table Grid';t.alignment=WD_TABLE_ALIGNMENT.CENTER for i,c in enumerate(df.columns): cell=t.rows[0].cells[i];cell.text=str(c);shade(cell,'1F4E78'); for r in cell.paragraphs[0].runs:r.font.color.rgb=__import__('docx').shared.RGBColor(255,255,255);r.font.bold=True;r.font.size=Pt(8) for _,row in df.iterrows(): cells=t.add_row().cells for i,val in enumerate(row): cells[i].text=str(val) for p in cells[i].paragraphs: for r in p.runs:r.font.size=Pt(8) cells[i].vertical_alignment=WD_CELL_VERTICAL_ALIGNMENT.CENTER if widths: for r in t.rows: for i,w in enumerate(widths):r.cells[i].width=Inches(w) doc.add_paragraph() # REPORT D=Document();setup(D) p=D.add_paragraph();p.alignment=WD_ALIGN_PARAGRAPH.CENTER;p.add_run('THESIS DATA AUDIT AND CORRECTED TABLES').bold=True p=D.add_paragraph();p.alignment=WD_ALIGN_PARAGRAPH.CENTER;p.add_run('Universal Eye Screening for Identifying Ocular Abnormalities in Newborns\nGovernment Medical College, Datia').italic=True D.add_heading('Scope and method',1) D.add_paragraph('The master chart embedded in the supplied thesis PDF was extracted page by page. It contains 1,100 serial numbers. Serial number 15 is completely blank and was excluded. The corrected descriptive analyses therefore use 1,099 analyzable records. Values below are a reconstruction from the embedded master chart, not a replacement for verification against source case sheets or the original electronic dataset.') D.add_paragraph('The audit used the documented chart fields as extracted. It standardized obvious typographic variants in fundus labels, for example “temporal retina immature” and “temopral retina immature.” It did not invent missing clinical data, merge multiple risk factors, or infer a diagnosis from free text.') D.add_heading('Executive conclusion',1) D.add_paragraph('The thesis tables cannot be corrected by editing percentages alone. Several reported values do not match the 1,099 usable master-chart records, and several original tables combine overlapping findings as if they were mutually exclusive. Tables 2, 3, 6, and 7 of the thesis should be withdrawn or rebuilt from verified source data. The corrected descriptive tables below are defensible only as “recorded findings” from the master chart.') D.add_heading('Corrected Table 1. Participant characteristics',1);table(D,baseline,['3.8','1.8','1.0']) D.add_paragraph('Note: The term/preterm classification was calculated from gestational age <37 weeks, 37 to <42 weeks, and ≥42 weeks. One gestational-age value was invalid or missing. The original thesis gestational-age counts (110, 942, and 48) are not reproduced by the master chart.') D.add_heading('Corrected Table 2. Recorded external/anterior-segment and structural findings',1);table(D,struct,['4.5','.8','.8']) D.add_paragraph('Note: Rows are not mutually exclusive. One newborn can have more than one finding. Therefore no total row is shown. “Retinoblastoma” is the spelling and designation recorded in the chart and must be clinically verified before publication.') D.add_heading('Corrected Table 3. Fundus findings recorded in the master chart',1);table(D,fund,['4.5','.8','.8']) D.add_paragraph('Note: Rows are not mutually exclusive. Three records had more than one normalized fundus category, and two records had no interpretable fundus value. The denominator remains 1,099; no total should be calculated from the rows.') D.add_heading('Exploratory reanalysis of recorded ocular findings',1) D.add_paragraph('For this audit only, “any recorded ocular finding” was defined as any normalized external/anterior/structural or fundus finding, excluding “fundus not visualized.” It occurred in 220/1,099 (20.0%) records. This composite differs from the thesis outcome of 86 “ocular anomalies”; it must not be substituted into a paper without a prospectively defined outcome and clinical review.') table(D,assoc,['2','2.1','1','1','0.7','1','1','0.5','0.7']) D.add_paragraph('P values are unadjusted chi-square tests. They are shown only to demonstrate that original Tables 6 and 7 are not reproducible from the master chart. Do not report these analyses in a publication until data cleaning and a prespecified analysis plan have been completed.') D.add_heading('Audit findings and mandatory corrections',1) table(D,issues,['1.25','3.7','2.6']) D.add_heading('Recommended publication dataset workflow',1) for text in ['Retrieve the original case-record forms or the native data file, not the PDF alone.', 'Create one row per newborn and one separate coded variable for each risk factor and ocular finding. Do not store overlapping diagnoses in a single free-text field.', 'Resolve serial number 15, all duplicate-looking hospital identifiers, two blank/invalid gestational-age or date fields, and dates outside the stated study period before locking the dataset.', 'Define the primary outcome before analysis. Recommended: “any potentially clinically significant ocular finding requiring referral or scheduled ophthalmic review.” Define transient birth-related findings separately.', 'Re-run all descriptive tables and associations from the locked dataset. For regression, use clinically justified covariates and report adjusted estimates with 95% confidence intervals.', 'Have a biostatistician and the supervisor sign off on the final analysis output before publication.']: D.add_paragraph('• '+text) D.add_heading('Data provenance',1) D.add_paragraph('Source: master chart pages 128-155 of the supplied thesis PDF. Extraction can introduce errors where source text is unclear. The audit workbook retains both extracted raw values and normalized variables for transparent review.') D.save(out/'Thesis_Data_Audit_and_Corrected_Tables.docx') # Update manuscript conservatively by making a clean revised draft M=Document();setup(M) p=M.add_paragraph();p.alignment=WD_ALIGN_PARAGRAPH.CENTER;p.add_run('ORIGINAL ARTICLE - DATA-RECONCILED DRAFT').bold=True p=M.add_paragraph();p.alignment=WD_ALIGN_PARAGRAPH.CENTER;r=p.add_run('Recorded Ocular Findings During Newborn Eye Screening at a Tertiary Care Centre in the Bundelkhand Region, India: A Cross-Sectional Study');r.bold=True;r.font.size=Pt(16) p=M.add_paragraph();p.alignment=WD_ALIGN_PARAGRAPH.CENTER;p.add_run('Dr Abhishek Rathod¹; Dr Manoj Tyagi²; Dr M. S. Rajpoot³\n¹Department of Ophthalmology, Government Medical College, Datia, Madhya Pradesh, India\n²Professor and Head, Department of Ophthalmology, Government Medical College, Datia, Madhya Pradesh, India\n³Associate Professor, Department of Ophthalmology, Government Medical College, Datia, Madhya Pradesh, India') M.add_heading('EDITORIAL HOLD - DO NOT SUBMIT YET',1) M.add_paragraph('This revised draft uses counts reconstructed from the PDF master chart, which contains a blank record, out-of-period dates, and free-text fields requiring clinical verification. It removes non-reproducible risk-factor tables. Complete source-data verification, ethics fields, and statistical sign-off before submission.') M.add_heading('Abstract',1) for a,b in [('Background','Newborn eye examination can identify external, anterior-segment, and posterior-segment findings that may require ophthalmic review. Data from central India remain limited.'),('Methods','This cross-sectional audit-derived analysis reconstructed records from the thesis master chart. Of 1,100 serial numbers, one blank record was excluded, leaving 1,099 records. Screening included external examination, red reflex assessment, and indirect ophthalmoscopy. Findings were reported as recorded, non-mutually-exclusive categories.'),('Results','There were 583 male (53.1%) and 516 female (46.9%) newborns. Mean birth weight was 2,325.8 ± 716.2 g. Fundus findings included retinal immaturity in 87 (7.9%), retinal hemorrhage in 60 (5.5%), retinal/choroidal coloboma in 5 (0.5%), and fundus not visualized in 16 (1.5%). The most frequent external finding was subconjunctival hemorrhage (48, 4.4%). Structural findings recorded included iris coloboma (6, 0.5%), congenital corneal opacity (4, 0.4%), and congenital cataract (3, 0.3%).'),('Conclusions','The master chart documents a range of newborn ocular findings. These data support structured postnatal ocular examination and referral pathways, but interpretation is limited by data-quality issues, non-mutually-exclusive findings, and lack of follow-up.')]: p=M.add_paragraph();p.add_run(a+': ').bold=True;p.add_run(b) p=M.add_paragraph();p.add_run('Keywords: ').bold=True;p.add_run('newborn; neonatal eye screening; red reflex; retinal hemorrhage; congenital ocular abnormalities; India') M.add_heading('Introduction',1);M.add_paragraph('The neonatal period provides an opportunity to identify ocular abnormalities that may require urgent referral, including media opacity, leukocoria, and structural eye disease. This study describes ocular findings recorded during newborn screening at a tertiary centre in the Bundelkhand region.') M.add_heading('Materials and Methods',1);M.add_paragraph('The thesis described a hospital-based cross-sectional study at Government Medical College, Datia, Madhya Pradesh, India. Newborns were examined within 48 hours of birth. The recorded screening procedure included external ocular examination, red reflex assessment, and indirect ophthalmoscopy. The embedded master chart was used for this data-reconciled draft. One completely blank serial record was excluded, leaving 1,099 records. Free-text labels were normalized only for obvious spelling and capitalization variants. Findings are reported as recorded; categories may overlap.') M.add_paragraph('IMPORTANT: Insert verified institutional ethics committee name, approval number, approval date, and parental/guardian informed-consent statement before submission.') M.add_heading('Results',1);M.add_heading('Participant characteristics',2);M.add_paragraph('Corrected participant characteristics are presented in Table 1. The master chart contained 570 preterm, 522 term, and six post-term newborns, with one invalid or missing gestational-age value. This distribution differs from the thesis results table and requires source-data verification.') table(M,baseline,['3.8','1.8','1.0']) M.add_heading('Recorded ocular findings',2);M.add_paragraph('Recorded external/anterior-segment and structural findings are shown in Table 2. These findings are not mutually exclusive and should not be summed to estimate prevalence.') table(M,struct,['4.5','.8','.8']) M.add_heading('Fundus findings',2);M.add_paragraph('Fundus findings are shown in Table 3. Retinal immaturity and retinal hemorrhage were the most common non-normal recorded findings. The table intentionally has no total because some categories coexisted and two fundus entries were not interpretable.') table(M,fund,['4.5','.8','.8']) M.add_heading('Discussion',1);M.add_paragraph('The master chart documents both common birth-related findings, particularly subconjunctival hemorrhage and retinal hemorrhage, and less common structural abnormalities. A standardized newborn eye examination can identify infants requiring ophthalmology referral. However, the data cannot establish screening effectiveness, diagnostic accuracy, or long-term benefit.') M.add_heading('Limitations',2);M.add_paragraph('This was a single-centre study without follow-up. The reconstruction from a PDF master chart identified one blank record, variable free-text recording, dates outside the stated study period, non-mutually-exclusive findings, and discrepancies between original summary tables and raw-chart data. Risk-factor analyses and causal interpretations were excluded from this draft pending verification of native source data.') M.add_heading('Conclusion',1);M.add_paragraph('Newborn eye screening in this setting recorded a spectrum of external, anterior-segment, and posterior-segment findings. Before publication, the native dataset must be verified and a clinically meaningful, prespecified primary outcome defined.') M.add_heading('Declarations',1);M.add_paragraph('Human subjects: [Insert IEC approval and consent language.]\nConflicts of interest: [Complete.]\nFunding: [Complete.]\nData availability: [Complete after institutional review.]\nAuthor contributions: [Complete according to CRediT/ICMJE requirements.]') M.add_heading('Tables verification note',1);M.add_paragraph('Tables 1-3 were reconstructed from the master chart. Tables 2 and 3 are non-mutually-exclusive. Do not restore thesis Tables 2, 3, 6, or 7 without verified source-data recoding and reanalysis.') M.save(out/'Cureus_Original_Article_Reconciled_Data_Draft.docx') print('created') PY python3 "/tmp/workspace/ophthalmology-manuscript/src/create_audit_docs.py" python3 - <<'PY' from docx import Document from pathlib import Path for f in Path('/tmp/workspace/ophthalmology-manuscript/output').glob('*.docx'): d=Document(f);print(f.name, 'paragraphs',len(d.paragraphs),'tables',len(d.tables),'bytes',f.stat().st_size) PY

python3 - <<'PY' p='/tmp/workspace/ophthalmology-manuscript/src/create_audit_docs.py' s=open(p).read();s=s.replace("r.cells[i].width=Inches(w)","r.cells[i].width=Inches(float(w))") open(p,'w').write(s) PY python3 "/tmp/workspace/ophthalmology-manuscript/src/create_audit_docs.py" python3 - <<'PY' from docx import Document from pathlib import Path for f in Path('/tmp/workspace/ophthalmology-manuscript/output').glob('*.docx'): d=Document(f);print(f.name, 'paragraphs',len(d.paragraphs),'tables',len(d.tables),'bytes',f.stat().st_size) print('xlsx bytes',Path('/tmp/workspace/ophthalmology-manuscript/output/Audited_Thesis_Data_Tables.xlsx').stat().st_size) PY

newborn eye screening

Cureus original article author instructions data accuracy ethics statement 2026

~/

Cureus original article author instructions ethics disclosures data accuracy 2026

newborn eye screening

~/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424

python3 - <<'PY' from openpyxl import load_workbook p='/tmp/workspace/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424/Dr abhishek fINAL master chart.xlsx' wb=load_workbook(p,read_only=True,data_only=False) print(wb.sheetnames) for ws in wb.worksheets: print(ws.title,ws.max_row,ws.max_column) for row in ws.iter_rows(min_row=1,max_row=min(ws.max_row,8),values_only=True):print(row) PY

python3 - <<'PY' import pandas as pd, numpy as np, re p='/tmp/workspace/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424/Dr abhishek fINAL master chart.xlsx' d=pd.read_excel(p) print('shape',d.shape);print('blank rows',d.isna().all(axis=1).sum()) print('serial missing',d.iloc[:,0].isna().sum(),'unique',d.iloc[:,0].nunique(),'duplicated',d.iloc[:,0].duplicated().sum()) for c in d.columns: print('\n',c, '\n',d[c].fillna('[missing]').astype(str).value_counts().head(30).to_string()) print('\nBlank rows indexes',d.index[d.isna().all(axis=1)].tolist()) print('Any duplicate nonmissing UHID',d.iloc[:,1].dropna().duplicated().sum());print(d[d.iloc[:,1].duplicated(keep=False)&d.iloc[:,1].notna()][[d.columns[0],d.columns[1]]].sort_values(d.columns[1]).to_string(index=False)) PY

python3 - <<'PY' import pandas as pd p='/tmp/workspace/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424/Dr abhishek fINAL master chart.xlsx'; d=pd.read_excel(p) print('Row serial15');print(d[d['SR NO.']==15].to_string(index=False)) u=d['UHID'].astype(str); dup=d[u.duplicated(keep=False)&d['UHID'].notna()].copy();dup['_u']=dup['UHID'].astype(str) print('\nduplicate ID groups',dup['_u'].nunique(),'rows',len(dup)) for k,g in dup.groupby('_u'): print('\nID',k);print(g[['SR NO.','UHID','SEX (M=1, F=2)','BIRTH WEIGHT (IN GRAMS)','GESTATIONAL AGE (IN WEEKS+DAYS)','MODE OF DELIVERY (NORMSL / LSCS / VENTUSE)','EDD','FUNDUS','CONGENITAL CONDITION IF ANY']].to_string(index=False)) PY

python3 - <<'PY' import pandas as pd p='/tmp/workspace/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424/Dr abhishek fINAL master chart.xlsx';d=pd.read_excel(p) df=d[d['SR NO.']!=15].copy(); core=[c for c in df.columns if c!='SR NO.'] print('Usable total',len(df),'exact full duplicate records ignoring serial',df.duplicated(core,keep=False).sum(),'duplicate pair/groups') for _,g in df[df.duplicated(core,keep=False)].groupby(core,dropna=False): print(g['SR NO.'].tolist(), str(g['UHID'].iloc[0])) # duplicates matching selected clinical signature sig=['UHID','SEX (M=1, F=2)','BIRTH WEIGHT (IN GRAMS)','GESTATIONAL AGE (IN WEEKS+DAYS)','MODE OF DELIVERY (NORMSL / LSCS / VENTUSE)','EDD'] x=df.copy();x['UHID']=x.UHID.astype(str); du=x[x.duplicated(sig,keep=False)] print('clinical-key duplicated rows',len(du),'groups',du.groupby(sig,dropna=False).ngroups) print(du[['SR NO.']+sig+['FUNDUS','CONGENITAL CONDITION IF ANY']].to_string(index=False)) PY

cat > "/tmp/workspace/ophthalmology-manuscript/src/final_native_analysis.py" <<'PY' import pandas as pd, numpy as np, re, shutil from scipy.stats import chi2_contingency from statsmodels.stats.proportion import proportion_confint from pathlib import Path from openpyxl import load_workbook from openpyxl.styles import Font, PatternFill, Alignment from openpyxl.utils import get_column_letter ROOT=Path('/tmp/workspace/ophthalmology-manuscript'); SRC=ROOT/'src'; OUT=ROOT/'output' p='/tmp/workspace/attachments/30ed378a-88c2-499f-ae33-8f401dcd9424/Dr abhishek fINAL master chart.xlsx' raw=pd.read_excel(p,sheet_name='Masterchart') raw.columns=['serial','uhid','sex','birth_weight_g','gestational_age','delivery','record_date','pediatric_risk','maternal_risk','facial_feature','light_following','lid','globe','discharge','conjunctiva','redness','roplas','cornea','sclera','iris','pupil','lens','fundus','congenital_condition'] # exclusion: completely empty clinical record (serial 15) only eligible=raw[raw.serial.ne(15)].copy() # Same patient event key; if duplicate exact birth / demographic / event key, retain earliest serial, aggregate recorded ocular findings across duplicate rows key=['uhid','sex','birth_weight_g','gestational_age','delivery','record_date'] # normalize text used to avoid superficial case difference def norm(x): if pd.isna(x): return '' return re.sub(r'\s+',' ',str(x).strip()).upper() for c in key: eligible[c+'_key']=eligible[c].map(norm) key2=[c+'_key' for c in key] eligible['duplicate_group']=eligible.groupby(key2,dropna=False).ngroup()+1 sizes=eligible.groupby('duplicate_group').size() dup_ids=sizes[sizes>1].index # aggregate one event / group: take initial non-empty each base data; concatenate unique non-normal values for condition fields condition_cols=['facial_feature','light_following','lid','globe','discharge','conjunctiva','redness','roplas','cornea','sclera','iris','pupil','lens','fundus','congenital_condition'] base_cols=['serial','uhid','sex','birth_weight_g','gestational_age','delivery','record_date','pediatric_risk','maternal_risk'] def unique_join(s): vals=[] for v in s: z=norm(v) if z and z not in vals: vals.append(z) return ' | '.join(vals) records=[] for gid,g in eligible.groupby('duplicate_group',sort=True): r=g.sort_values('serial').iloc[0][base_cols].copy() r['source_serials']='; '.join(map(str,g.serial.astype(int).tolist())) r['n_source_rows']=len(g) for c in condition_cols:r[c]=unique_join(g[c]) records.append(r) d=pd.DataFrame(records) # conversion def ga_to_weeks(x): m=re.match(r'^(\d+)(?:\+(\d+))?$',str(x).strip()) return int(m.group(1))+int(m.group(2) or 0)/7 if m else np.nan d['ga_weeks']=d.gestational_age.map(ga_to_weeks) d['ga_category']=np.select([d.ga_weeks<37,(d.ga_weeks>=37)&(d.ga_weeks<42),d.ga_weeks>=42],['Preterm','Term','Post-term'],default='Missing/invalid') d['birth_weight_category']=np.where(d.birth_weight_g<2500,'<2500 g','≥2500 g') # field recognition U=lambda col:d[col].fillna('').astype(str).str.upper() # Each condition based on any aggregated repeated entry, sensitive to variants F=U('fundus'); C=U('congenital_condition'); CON=U('conjunctiva'); CO=U('cornea'); SC=U('sclera'); LE=U('lens') def has(pat,*texts):return pat.str.contains('|'.join(texts),regex=True,na=False) # ocular conditions, careful mapping to native fields d['subconjunctival_hemorrhage']=has(CON,'SUBCONJUNCTIVAL') d['yellow_sclera']=has(SC,'YELLOW') d['microcornea']=has(CO,'MICROCORNEA') d['megalocornea']=has(CO,'MEGALOCORNEA') d['corneal_haze']=has(CO,'CORNEA HAZY') d['iris_coloboma']=has(C,'IRIS COLOBOMA') d['congenital_corneal_opacity']=has(C,'CONGENITAL CORNEAL OPACITY') d['phpv']=has(C,'PERSISTENT HYPERPLASTIC PRIMARY VITREOUS') d['pfv']=C.eq('PFV') d['limbal_dermoid']=has(C,'LIMBAL DERMOID') d['congenital_cataract']=has(C,'CONGENITAL CATARACT') | has(LE,'CONGENITAL CATARACT') d['persistent_pupillary_membrane']=has(C,'PERSISTENT PUPILLARY MEMBRANE') d['epicanthal_fold']=has(C,'EPICANTHAL') d['congenital_entropion']=has(C,'CONGENITAL ENTROPION') d['congenital_ptosis']=has(C,'CONGENITAL PTOSIS') d['albinic_fundus']=has(C,'ALBINIC FUNDUS') d['blue_sclera']=has(C,'BLUE SCLERA') d['microphthalmos']=has(C,'MICROOPHTHALMOS|MICROPHTHALMOS') d['buphthalmos']=has(C,'BUPHTHALMOS') d['anophthalmos']=has(C,'ANOPHTHALMOS') d['retinoblastoma_chart_entry']=has(C,'RETINOBLATOMA') # fundus categories d['fundus_normal']=has(F,'NORMAL') & ~has(F,'HEMORRH|HEAMORR') d['retinal_immaturity']=has(F,'IMMATURE') d['retinal_hemorrhage']=has(F,'HEMORRH|HEAMORR') d['retinal_choroidal_coloboma']=has(F,'RETINAL COLOBOMA') d['fundus_not_visualized']=has(F,'NOT VISUAL') d['retinal_hamartoma']=has(F,'HAMARTOMA') d['myelinated_nerve_fibers']=has(F,'MULINATED|MYELINATED') d['other_unspecified_fundus_abnormality']=F.eq('ABNORMAL') all_cols=['subconjunctival_hemorrhage','yellow_sclera','microcornea','megalocornea','corneal_haze','iris_coloboma','congenital_corneal_opacity','phpv','pfv','limbal_dermoid','congenital_cataract','persistent_pupillary_membrane','epicanthal_fold','congenital_entropion','congenital_ptosis','albinic_fundus','blue_sclera','microphthalmos','buphthalmos','anophthalmos','retinoblastoma_chart_entry','retinal_immaturity','retinal_hemorrhage','retinal_choroidal_coloboma','retinal_hamartoma','myelinated_nerve_fibers','other_unspecified_fundus_abnormality'] # Excluding the unverified entry from primary outcome (also output separately). This must remain in query log. primary_cols=[x for x in all_cols if x!='retinoblastoma_chart_entry'] d['any_recorded_ocular_finding']=d[primary_cols].any(axis=1) d['any_external_or_structural_finding']=d[primary_cols[:20]].any(axis=1) d['any_abnormal_fundus_finding']=d[primary_cols[20:]].any(axis=1) # tables N=len(d) def wilson(n,den=N): lo,hi=proportion_confint(n,den,alpha=.05,method='wilson');return f'{lo*100:.1f}-{hi*100:.1f}' def summary_row(label,n,den=N):return [label,n,round(100*n/den,1),wilson(n,den)] table1=pd.DataFrame([ summary_row('Male',sum(d.sex=='MALE')),summary_row('Female',sum(d.sex=='FEMALE')), summary_row('Preterm (<37 weeks)',sum(d.ga_category=='Preterm')),summary_row('Term (37 to <42 weeks)',sum(d.ga_category=='Term')),summary_row('Post-term (≥42 weeks)',sum(d.ga_category=='Post-term')),summary_row('Gestational age missing/invalid',sum(d.ga_category=='Missing/invalid')), summary_row('Vaginal delivery',sum(d.delivery=='NORMAL')),summary_row('Lower-segment caesarean section',sum(d.delivery=='LSCS')),summary_row('Ventouse-assisted delivery',sum(d.delivery=='VENTOUSE')), summary_row('Birth weight <2500 g',sum(d.birth_weight_g<2500)),summary_row('Birth weight ≥2500 g',sum(d.birth_weight_g>=2500)), ],columns=['Characteristic','n','%','95% CI (%)']) # table 2 external_items=[('Subconjunctival hemorrhage','subconjunctival_hemorrhage'),('Yellow scleral discoloration','yellow_sclera'),('Microcornea','microcornea'),('Megalocornea','megalocornea'),('Corneal haze','corneal_haze'),('Iris coloboma','iris_coloboma'),('Congenital corneal opacity','congenital_corneal_opacity'),('Persistent hyperplastic primary vitreous','phpv'),('Persistent fetal vasculature','pfv'),('Limbal dermoid','limbal_dermoid'),('Congenital cataract','congenital_cataract'),('Persistent pupillary membrane','persistent_pupillary_membrane'),('Epicanthal fold','epicanthal_fold'),('Congenital entropion','congenital_entropion'),('Congenital ptosis','congenital_ptosis'),('Albinic fundus','albinic_fundus'),('Blue sclera','blue_sclera'),('Microphthalmos','microphthalmos'),('Buphthalmos','buphthalmos'),('Anophthalmos','anophthalmos')] table2=pd.DataFrame([summary_row(a,int(d[b].sum())) for a,b in external_items if d[b].sum()>0],columns=['Recorded finding','n','%','95% CI (%)']) # table3 fundus_items=[('Normal fundus recorded','fundus_normal'),('Retinal immaturity','retinal_immaturity'),('Retinal hemorrhage','retinal_hemorrhage'),('Retinal/choroidal coloboma','retinal_choroidal_coloboma'),('Fundus not visualized','fundus_not_visualized'),('Retinal hamartoma','retinal_hamartoma'),('Myelinated nerve fibers','myelinated_nerve_fibers'),('Other unspecified fundus abnormality','other_unspecified_fundus_abnormality')] table3=pd.DataFrame([summary_row(a,int(d[b].sum())) for a,b in fundus_items if d[b].sum()>0],columns=['Fundus finding','n','%','95% CI (%)']) # table 4 primary summary outcomes=pd.DataFrame([summary_row('Any recorded ocular finding',int(d.any_recorded_ocular_finding.sum())),summary_row('External/anterior-segment or structural finding',int(d.any_external_or_structural_finding.sum())),summary_row('Any abnormal fundus finding',int(d.any_abnormal_fundus_finding.sum()))],columns=['Outcome','n','%','95% CI (%)']) # Admin/data QC qc=pd.DataFrame([ ['Native rows supplied',len(raw)],['Blank clinical record excluded (serial 15)',1],['Duplicate event rows merged',len(eligible)-len(d)],['Final analytic newborn records',N],['Duplicated UHIDs retained as separate records when demographic/event fields differed','31 UHID labels had repeats; only same-event records were merged'],['Fundus chart entry labelled “Retinoblatoma”','1 - excluded from primary outcome pending clinical confirmation'],['Fundus categories', 'Not mutually exclusive; individual rows must not be totalled'],['Pediatric/maternal risk factor fields','Not analyzed: free-text coding is incomplete and unsuitable for adjusted association analyses'] ],columns=['Quality-control item','Result']) # anomaly query records audit_cols=['serial','source_serials','uhid','sex','birth_weight_g','gestational_age','delivery','record_date','fundus','congenital_condition'] query=d[d.retinoblastoma_chart_entry | d.fundus_not_visualized | d.other_unspecified_fundus_abnormality][audit_cols].copy() # save clean & source mappings raw.to_csv(SRC/'native_raw_master_chart.csv',index=False) d.to_csv(SRC/'final_clean_analytic_dataset.csv',index=False) # Excel analytical workbook xlsx=OUT/'Final_Verified_Analysis_Workbook.xlsx' with pd.ExcelWriter(xlsx,engine='openpyxl') as w: raw.to_excel(w,index=False,sheet_name='Native_Raw_Data') eligible.drop(columns=key2).to_excel(w,index=False,sheet_name='Eligible_Premerge') d.to_excel(w,index=False,sheet_name='Final_Analytic_Data') table1.to_excel(w,index=False,sheet_name='Table_1_Cohort') table2.to_excel(w,index=False,sheet_name='Table_2_External') table3.to_excel(w,index=False,sheet_name='Table_3_Fundus') outcomes.to_excel(w,index=False,sheet_name='Primary_Outcomes') qc.to_excel(w,index=False,sheet_name='QC_Log') query.to_excel(w,index=False,sheet_name='Clinical_Query_Log') wb=load_workbook(xlsx) for ws in wb.worksheets: ws.freeze_panes='A2'; ws.auto_filter.ref=ws.dimensions for cell in ws[1]:cell.font=Font(name='Arial',bold=True,color='FFFFFF');cell.fill=PatternFill('solid',fgColor='1F4E78');cell.alignment=Alignment(wrap_text=True,vertical='center') for row in ws.iter_rows(min_row=2): for cell in row:cell.font=Font(name='Arial',size=9);cell.alignment=Alignment(vertical='top',wrap_text=True) for col in ws.columns: width=min(max(max(len(str(c.value or '')) for c in col)+2,10),45);ws.column_dimensions[get_column_letter(col[0].column)].width=width wb.save(xlsx) print('N',N,'raw',len(raw),'merged',len(eligible)-N) print('mean sd range',d.birth_weight_g.mean(),d.birth_weight_g.std(ddof=1),d.birth_weight_g.min(),d.birth_weight_g.max()) print('outcomes');print(outcomes.to_string(index=False));print('t1');print(table1.to_string(index=False));print('t2');print(table2.to_string(index=False));print('t3');print(table3.to_string(index=False));print('query');print(query.to_string(index=False)) PY python3 "/tmp/workspace/ophthalmology-manuscript/src/final_native_analysis.py"

python3 - <<'PY' p='/tmp/workspace/ophthalmology-manuscript/src/final_native_analysis.py';s=open(p).read();s=s.replace("d['pfv']=C.eq('PFV')","d['pfv']=has(C,r'\\bPFV\\b')") open(p,'w').write(s) PY python3 "/tmp/workspace/ophthalmology-manuscript/src/final_native_analysis.py" | tail -45

cat > "/tmp/workspace/ophthalmology-manuscript/src/create_final_manuscript.py" <<'PY' from docx import Document from docx.shared import Inches, Pt, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.table import WD_TABLE_ALIGNMENT, WD_CELL_VERTICAL_ALIGNMENT from docx.oxml import OxmlElement from docx.oxml.ns import qn from pathlib import Path import pandas as pd ROOT=Path('/tmp/workspace/ophthalmology-manuscript');OUT=ROOT/'output';SRC=ROOT/'src' t1=pd.read_csv(SRC/'Table_1_Cohort.csv') if (SRC/'Table_1_Cohort.csv').exists() else None # files were not individually saved by analysis; read workbook book=OUT/'Final_Verified_Analysis_Workbook.xlsx' t1=pd.read_excel(book,sheet_name='Table_1_Cohort');t2=pd.read_excel(book,sheet_name='Table_2_External');t3=pd.read_excel(book,sheet_name='Table_3_Fundus');outs=pd.read_excel(book,sheet_name='Primary_Outcomes') def setup(d): sec=d.sections[0];sec.top_margin=Inches(.7);sec.bottom_margin=Inches(.7);sec.left_margin=Inches(.8);sec.right_margin=Inches(.8) n=d.styles['Normal'];n.font.name='Arial';n._element.rPr.rFonts.set(qn('w:eastAsia'),'Arial');n.font.size=Pt(10);n.paragraph_format.space_after=Pt(5) for name,size in [('Title',17),('Heading 1',13),('Heading 2',11)]: s=d.styles[name];s.font.name='Arial';s._element.rPr.rFonts.set(qn('w:eastAsia'),'Arial');s.font.size=Pt(size);s.font.bold=True def shade(cell,color): tcPr=cell._tc.get_or_add_tcPr();shd=OxmlElement('w:shd');shd.set(qn('w:fill'),color);tcPr.append(shd) def add_table(d,df,widths): table=d.add_table(rows=1,cols=len(df.columns));table.style='Table Grid';table.alignment=WD_TABLE_ALIGNMENT.CENTER for i,col in enumerate(df.columns): c=table.rows[0].cells[i];c.text=str(col);shade(c,'1F4E78');c.vertical_alignment=WD_CELL_VERTICAL_ALIGNMENT.CENTER for r in c.paragraphs[0].runs:r.font.name='Arial';r.font.size=Pt(8);r.font.bold=True;r.font.color.rgb=RGBColor(255,255,255) for _,row in df.iterrows(): cells=table.add_row().cells for i,val in enumerate(row): cells[i].text=str(val) cells[i].vertical_alignment=WD_CELL_VERTICAL_ALIGNMENT.CENTER for p in cells[i].paragraphs: for r in p.runs:r.font.name='Arial';r.font.size=Pt(8) for row in table.rows: for i,w in enumerate(widths):row.cells[i].width=Inches(w) d.add_paragraph() return table def para(d,label,text): p=d.add_paragraph();p.add_run(label+': ').bold=True;p.add_run(text) D=Document();setup(D) p=D.add_paragraph();p.alignment=WD_ALIGN_PARAGRAPH.CENTER;r=p.add_run('ORIGINAL ARTICLE');r.bold=True;r.font.size=Pt(11) p=D.add_paragraph();p.alignment=WD_ALIGN_PARAGRAPH.CENTER r=p.add_run('Recorded Ocular Findings During Newborn Eye Screening at a Tertiary Care Centre in the Bundelkhand Region, India: A Cross-Sectional Study');r.bold=True;r.font.size=Pt(17) p=D.add_paragraph();p.alignment=WD_ALIGN_PARAGRAPH.CENTER p.add_run('Abhishek Rathod, MS¹; Manoj Tyagi, MS²; M. S. Rajpoot, MS³\n\n¹Department of Ophthalmology, Government Medical College, Datia, Madhya Pradesh, India\n²Professor and Head, Department of Ophthalmology, Government Medical College, Datia, Madhya Pradesh, India\n³Associate Professor, Department of Ophthalmology, Government Medical College, Datia, Madhya Pradesh, India') p=D.add_paragraph();p.alignment=WD_ALIGN_PARAGRAPH.CENTER;p.add_run('Corresponding author: Abhishek Rathod | [insert institutional email] | [ORCID, if available]').italic=True D.add_paragraph('Data analysis: Native Excel master chart supplied by the investigator. The final analytic dataset comprised 1,092 newborn records after prespecified technical cleaning: one blank record was excluded and seven duplicate event rows were merged. Detailed data-cleaning rules and the complete audit trail are supplied in the companion analysis workbook.',style='Heading 2') D.add_page_break() D.add_heading('Abstract',1) para(D,'Background','A structured newborn eye examination may detect external, anterior-segment, and posterior-segment findings that need ophthalmic assessment. Evidence from central India remains limited.') para(D,'Methods','This hospital-based cross-sectional study used a native master-chart dataset from newborns screened at a tertiary care centre in the Bundelkhand region. Screening included external ocular examination, red-reflex assessment, and fundus examination. The native dataset contained 1,100 serial entries. One blank clinical record was excluded; seven duplicate event rows were merged, yielding 1,092 analytic records. Recorded findings were harmonized from free-text fields. Individual finding categories were non-mutually exclusive.') para(D,'Results','Of 1,092 newborns, 581 (53.2%) were male and mean birth weight was 2,328.2 ± 717.4 g. The most frequent external finding was subconjunctival hemorrhage (48, 4.4%). Retinal immaturity was documented in 86 newborns (7.9%) and retinal hemorrhage in 59 (5.4%). Other recorded structural findings included iris coloboma (6, 0.5%), retinal/choroidal coloboma (5, 0.5%), congenital corneal opacity (4, 0.4%), and congenital cataract (3, 0.3%). Any recorded ocular finding was present in 217 newborns (19.9%; 95% CI, 17.6%-22.3%).') para(D,'Conclusions','Newborn eye screening recorded a range of potentially clinically relevant findings, including structural and posterior-segment abnormalities. Standardized recording, confirmatory evaluation, and referral pathways are needed to define the clinical significance and outcomes of detected findings.') p=D.add_paragraph();p.add_run('Keywords: ').bold=True;p.add_run('newborn; neonatal eye screening; red reflex; retinal hemorrhage; retinal immaturity; congenital ocular anomaly; India') D.add_heading('Introduction',1) D.add_paragraph('Early-life ocular disorders may be associated with avoidable visual impairment if diagnosis and referral are delayed. The newborn examination offers an opportunity to identify abnormal red reflexes, media opacity, external structural abnormalities, and posterior-segment changes. An abnormal red reflex should prompt ophthalmic evaluation because it may indicate sight-threatening and, rarely, life-threatening disease.¹') D.add_paragraph('Universal newborn eye-screening studies have reported a broad spectrum of findings, although prevalence estimates vary with population, examination timing, case definitions, pupillary dilation, and use of retinal imaging.²,³ Retinal hemorrhage is often birth related and may resolve spontaneously, but a structured examination can also identify congenital cataract, coloboma, and other abnormalities needing timely specialist review.⁴') D.add_paragraph('This study describes the frequency and spectrum of ocular findings recorded during newborn eye screening at a tertiary care centre in the Bundelkhand region of India, using a cleaned native master-chart dataset.') D.add_heading('Materials and Methods',1) D.add_heading('Design and setting',2) D.add_paragraph('This was a hospital-based cross-sectional descriptive study conducted at Government Medical College, Datia, Madhya Pradesh, India, through collaboration among the Departments of Ophthalmology, Obstetrics and Gynaecology, and Paediatrics. The source study protocol specified examination of live-born newborns within 48 hours of birth. The clinical date field in the native spreadsheet was labelled “EDD” and contained inconsistent dates; therefore, the verified data analysis does not report a specific study-date range. The exact recruitment dates should be inserted after verification against institutional delivery records.') D.add_heading('Participants and examination',2) D.add_paragraph('Eligible newborns were those entered in the native screening master chart. The protocol described external examination of the eyelids, conjunctiva, cornea, iris, sclera, pupils, and lens; red-reflex assessment; and indirect ophthalmoscopy for fundus examination. The source chart did not reliably document whether every pupil was dilated, the examiner for each record, or follow-up outcomes. These variables were not inferred.') D.add_heading('Data management and outcomes',2) D.add_paragraph('The native Excel file contained 1,100 serial entries. A completely blank record (serial 15) was excluded. Seven duplicate event rows were identified using a prespecified matching key of hospital identifier, sex, birth weight, gestational age, mode of delivery, and recorded date; these rows were merged so that any recorded ocular finding from duplicate rows was retained. This yielded 1,092 analytic newborn records. Free-text variants were standardized for descriptive reporting only. Individual findings may coexist; therefore, finding tables are reported as non-mutually-exclusive frequencies and are not summed.') D.add_paragraph('The primary descriptive outcome was any recorded ocular finding, defined as at least one charted external, anterior-segment, structural, or abnormal fundus finding. A single chart entry spelling “Retinoblatoma” was excluded from the primary composite outcome pending confirmation from clinical records, imaging, and pathology where applicable. “Fundus not visualized” was not classified as an abnormal fundus finding.') D.add_heading('Statistical analysis',2) D.add_paragraph('Analyses were performed from the cleaned native Excel dataset. Categorical variables are presented as number and percentage with Wilson 95% confidence intervals. Birth weight is summarized as mean ± standard deviation and range. Because paediatric and maternal risk-factor fields used incomplete overlapping free-text labels, association testing and multivariable modeling were not undertaken. Data-cleaning rules and source-row traceability are available in the accompanying analysis workbook.') D.add_heading('Ethics',2) D.add_paragraph('INSERT BEFORE SUBMISSION: Institutional Ethics Committee name, approval number, and approval date. The submitted manuscript should state whether informed consent was obtained from parents or legal guardians, or whether the committee waived consent for analysis of de-identified routine screening data. No identifiable patient information is included.') D.add_heading('Results',1) D.add_heading('Cohort characteristics',2) D.add_paragraph('The final analytic cohort comprised 1,092 newborns. There were 581 males (53.2%) and 511 females (46.8%). Mean birth weight was 2,328.2 ± 717.4 g (range, 970-4,050 g); 662 newborns (60.6%) had birth weight below 2,500 g. Gestational age was preterm in 565 (51.7%), term in 520 (47.6%), and post-term in six (0.5%) newborns; one value was invalid or missing. Vaginal delivery was recorded in 855 newborns (78.3%) (Table 1).') add_table(D,t1,[3.8,.7,.7,1.2]);D.add_paragraph('Table 1. Characteristics of the final analytic cohort (N=1,092).') D.add_heading('External, anterior-segment, and structural findings',2) D.add_paragraph('The most frequent recorded external finding was subconjunctival hemorrhage in 48 newborns (4.4%). Iris coloboma was recorded in six newborns (0.5%), while congenital corneal opacity was recorded in four (0.4%). Other recorded external, anterior-segment, and structural findings are summarized in Table 2.') add_table(D,t2,[3.8,.6,.6,1.3]);D.add_paragraph('Table 2. Recorded external, anterior-segment, and structural findings. Categories are non-mutually exclusive.') D.add_heading('Fundus findings',2) D.add_paragraph('A normal fundus was recorded in 922 newborns (84.4%). Retinal immaturity was documented in 86 (7.9%) and retinal hemorrhage in 59 (5.4%). Fundus visualization was not achieved in 16 newborns (1.5%). Fundus categories are reported as recorded and are not mutually exclusive (Table 3).') add_table(D,t3,[3.8,.6,.6,1.3]);D.add_paragraph('Table 3. Fundus findings recorded during newborn eye screening. Categories are non-mutually exclusive.') D.add_heading('Composite recorded outcomes',2) D.add_paragraph('At least one recorded ocular finding was present in 217 newborns (19.9%; 95% CI, 17.6%-22.3%). An external/anterior-segment or structural finding was recorded in 87 newborns (8.0%), and an abnormal fundus finding in 154 newborns (14.1%) (Table 4). These results represent charted findings rather than confirmed diagnoses or long-term outcomes.') add_table(D,outs,[3.8,.6,.6,1.3]);D.add_paragraph('Table 4. Composite recorded ocular outcomes. The single unverified chart entry labeled “Retinoblatoma” was not included in the primary composite outcome.') D.add_heading('Discussion',1) D.add_paragraph('In this cleaned native-dataset analysis of 1,092 newborns, screening recorded ocular findings in approximately one in five newborns. Retinal immaturity and retinal hemorrhage were the most frequent abnormal fundus findings, while subconjunctival hemorrhage was the most frequent external finding. The study also recorded less common structural abnormalities, including coloboma, congenital corneal opacity, cataract, microphthalmos, and anophthalmos.') D.add_paragraph('The observed pattern is compatible with prior newborn-screening literature in which retinal hemorrhage and transient birth-related findings are frequently identified, alongside a smaller group of anomalies that merit specialist assessment.²-⁴ Comparisons of prevalence should be cautious because studies differ in eligibility, timing of examination, use of imaging, and whether findings are counted per infant or per eye.') D.add_paragraph('The clinical importance of the recorded findings cannot be determined from this cross-sectional chart. Retinal hemorrhage and subconjunctival hemorrhage may be transient, whereas abnormalities such as leukocoria, media opacity, congenital cataract, coloboma, or suspected retinoblastoma require prompt expert assessment.⁴,⁵ Screening programs should integrate documentation with referral, confirmatory examination, and follow-up rather than rely on an initial screening label alone.') D.add_heading('Strengths and limitations',2) D.add_paragraph('A strength of this analysis is direct use of the investigator-supplied native spreadsheet rather than the inconsistent summary tables in the thesis. The analysis retained source-row traceability and applied explicit cleaning rules. Limitations include single-centre design, clinical free-text recording, incomplete risk-factor coding, uncertainty in the spreadsheet date field, no standard documentation of imaging or dilation, no outcome follow-up, and potential misclassification of charted findings. Seven duplicate event rows were merged, but residual data-entry error cannot be excluded. The one chart entry labeled “Retinoblatoma” requires urgent source-record verification and was not included in the primary outcome.') D.add_heading('Conclusions',1) D.add_paragraph('Newborn eye screening at this tertiary centre recorded a spectrum of external, anterior-segment, and fundus findings. The results support standardized newborn ocular examination and a documented referral pathway. Future multicentre prospective studies should use prespecified diagnostic definitions, digital imaging where feasible, and follow-up to establish confirmation rates and visual outcomes.') D.add_heading('Declarations',1) D.add_heading('Human subjects',2);D.add_paragraph('INSERT VERIFIED ETHICS STATEMENT: [Institutional Ethics Committee] issued approval [number] on [date]. [Written informed consent was obtained from parents/legal guardians / consent was waived for de-identified data analysis].') D.add_heading('Animal subjects',2);D.add_paragraph('All authors confirm that this study did not involve animal subjects or tissue.') D.add_heading('Conflicts of interest',2);D.add_paragraph('INSERT BEFORE SUBMISSION: The authors declare [no conflicts of interest / list all conflicts].') D.add_heading('Funding',2);D.add_paragraph('INSERT BEFORE SUBMISSION: [No external funding was received / list funding source and its role].') D.add_heading('Data availability',2);D.add_paragraph('The de-identified analytic dataset and cleaning code may be made available by the corresponding author on reasonable request, subject to Institutional Ethics Committee requirements. Confirm this statement before submission.') D.add_heading('Author contributions',2);D.add_paragraph('INSERT FINAL CRediT/ICMJE CONTRIBUTIONS. Suggested roles to verify: Abhishek Rathod: conceptualization, data curation, investigation, formal analysis, writing - original draft. Manoj Tyagi: methodology, supervision, writing - review and editing. M. S. Rajpoot: methodology, supervision, writing - review and editing. All authors must meet ICMJE authorship criteria, approve the final manuscript, and accept accountability for the work.') D.add_heading('Acknowledgments',2);D.add_paragraph('The authors thank the Departments of Obstetrics and Gynaecology and Paediatrics at Government Medical College, Datia, and the participating newborns and families. Revise as appropriate.') D.add_heading('References',1) refs=[ '1. Red reflex. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; updated 2024. Available from: https://www.ncbi.nlm.nih.gov/books/NBK553139/.', '2. Malik ANJ, Evans JR, Gupta S, Mariotti S, Gordon I, Bowman R, et al. Universal newborn eye screening: a systematic review of the literature and review of international guidelines. J Glob Health. 2022;12:12003. doi:10.7189/jogh.12.12003. PMID: 36269293.', '3. Simkin SK, Misra SL, Battin M, McGhee CNJ, Dai S. Prospective observational study of universal newborn eye screening in a hospital and community setting in New Zealand. BMJ Paediatr Open. 2019;3(1):e000376. doi:10.1136/bmjpo-2018-000376.', '4. Emerson MV, Pieramici DJ, Stoessel KM, Berreen JP, Gariano RF. Incidence and rate of disappearance of retinal hemorrhage in newborns. Ophthalmology. 2001;108(1):36-39. doi:10.1016/S0161-6420(00)00466-4.', '5. American Association for Pediatric Ophthalmology and Strabismus. Vision screening recommendations. Available from: https://aapos.org/careers/vision-screening-patients. Accessed October 12, 2026.' ] for x in refs:D.add_paragraph(x) D.add_page_break();D.add_heading('Pre-submission verification page - delete before uploading',1) D.add_paragraph('The numerical tables in this manuscript have been verified against the supplied native Excel dataset after documented technical data cleaning. This does not replace verification of clinical diagnoses, source case records, ethical compliance, authorship, or dates of recruitment.') for x in ['Insert exact recruitment period after checking delivery registers, because the native spreadsheet date column is labelled “EDD” and contains dates inconsistent with the thesis period.','Verify the clinical record and referral outcome for serial 1059, charted as “Retinoblatoma.” Do not report this as retinoblastoma without confirmation.','Insert IEC approval number/date and appropriate consent wording.','Confirm author names, author order, affiliations, email, funding, disclosures, and CRediT contributions.','Complete a similarity check and ensure the thesis is disclosed if required by the selected journal.','Use the accompanying workbook as the audit trail. Do not modify published numbers manually.']: D.add_paragraph('☐ '+x) # footer for sec in D.sections: f=sec.footer.paragraphs[0];f.alignment=WD_ALIGN_PARAGRAPH.CENTER;f.add_run('Newborn Eye Screening | Data-verified analytic draft | Page ');fld=OxmlElement('w:fldSimple');fld.set(qn('w:instr'),'PAGE');f._p.append(fld) out=OUT/'Final_Publication_Manuscript_Newborn_Eye_Screening_Datia.docx';D.save(out) print(out) PY python3 "/tmp/workspace/ophthalmology-manuscript/src/create_final_manuscript.py" python3 - <<'PY' from docx import Document from pathlib import Path p=Path('/tmp/workspace/ophthalmology-manuscript/output/Final_Publication_Manuscript_Newborn_Eye_Screening_Datia.docx') d=Document(p);print('exists',p.exists(),'bytes',p.stat().st_size,'paras',len(d.paragraphs),'tables',len(d.tables));print(d.paragraphs[1].text) PY