Childhood blindness and visual impairment in the Narayani Zone of Nepal: a population-based survey
Bibliographic record
Abstract
Purpose: To estimate the prevalence and causes of blindness (BL), severe visual impairment (SVI), moderate visual impairment (ModVI) and mild visual impairment (MildVI) in children in Narayani Zone, Nepal.Methods: In 2017, 100 population clusters within the Narayani Zone of Nepal were selected using RAAB software. Children (aged 0–15 years) suspected of having visual problems were identified using Key Informants (KIs) and school teachers and were referred for ophthalmologic examination. Eye care staff actively sought children who failed to present for examination. Causes of BL/SVI/ModVI/MildVI were categorized using standard World Health Organization definitions.Results: Of 76,588 children selected, 72,900 (95%) were screened. Of 2,158 children referred for examination, 1,322 were referred by teachers and 836 by KIs. A total of 1,617 (75%) children received a detailed examination, of whom 128 children [65 girls (51%)] mean age of 9.4 (± 4.1 years) were confirmed to have BL 7 (5.5%), SVI 16 (12.5%), ModVI 19 (15%) or MildVI 86 (67%). The combined prevalence of BL/SVI/ModVI/MildVI was 175/100,000 (95% CI 172–178/100,000); BL/SVI/ModVI was 55/100,000 (95% CI 53–57/100,000) and the combined BL/SVI estimate was 30/100,000 (95% CI 29–31/100,000). The leading causes of BL/SVI/MVI were refractive error 23 (55%) and whole globe disorders 5 (12%). Total avoidable causes were 31 (74%).Conclusion: The prevalence of BL/SVI/ModVI among children in Narayani Zone was moderate and included a high proportion of avoidable and treatable cases. Pediatric ophthalmic services need improvement, mainly refractive error correction in rural areas of Nepal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".