Blindness prevalence and cataract surgical coverage in Lumbini Zone and Chetwan District of Nepal
Bibliographic record
Abstract
AIM: To determine the prevalence of blindness, visual impairment and the cataract surgical coverage for people aged 50 years and older in the Lumbini Zone and the Chitwan District (Narayani Zone) of Nepal. METHODS: A population-based cross-sectional study in 2006 selected subjects aged 50 years and older through a random multistage cluster sampling and door-to-door enumeration. Ophthalmic examination included visual-acuity assessment and refraction, and anterior and posterior segment examination of the eyes carried out by a trained ophthalmologist and two ophthalmic assistants at centralised locations. RESULTS: The survey examined 5138 of 5196 persons enumerated (response rate of 86.8%). The mean age of the subjects was 61 (SD 9.2) years, and 2701 (52.6%) subjects were women. The age-sex-adjusted prevalence of blindness (best presenting vision <6/60) and visual impairment (better-eye presenting visual acuity of <6/18 to > or = 6/60) were 4.6% (95% CI 3.4 to 5.8) and 18.9% (95% CI 16.4 to 21.4), respectively. Blindness was significantly lower in the hill (3.3%) compared with the plain (5.8%) regions (OR 0.6; 95% CI 0.4 to 0.9). The primary causes for blind eyes were cataract (n = 228, 48.1%), refractive error (n = 149, 31.4%), retinal disorders (n = 19, 4.0%) and corneal opacity (n = 18, 3.8%). The overall cataract surgical coverage was 66.6%. Cataract surgical coverage was not significantly associated with age, sex, literacy or District. CONCLUSION: Although the prevalence of blindness and visual impairment is lower than 10 years ago, particularly among women, correctable blindness due to cataract and refractive error (79.5% of blind people) remains a significant population health problem in Lumbini Zone and Chitwan District.
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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 | 0.000 |
| 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".