Cataract Surgical Outcome and Predictors of Outcome in Lumbini Zone and Chitwan District of Nepal
Bibliographic record
Abstract
PURPOSE: To evaluate visual acuity outcome from cataract surgery based on a population-based survey among people aged 50 years and older in Lumbini Zone and Chitwan District of Nepal. METHODS: A randomly selected, population-based cross sectional epidemiological study of blindness, visual impairment and cataract surgical outcome was conducted. All subjects underwent a comprehensive ocular examination by an ophthalmic assistant, while people with visual impairment (visual acuity less than 6/18) after refraction and all cataract surgical cases underwent dilated fundus examination by an ophthalmologist. RESULTS: 5,916 people were enumerated and 5,141 (86.9%) examined. Among the 359 people who had cataract surgery, 485 eyes were included in the study. First eye surgery was before 2000 in 84 (23%), between 2000 and 2003 in 130 (36%), and after 2003 in 145 (41%). A presenting visual acuity ≥ 6/18 was achieved in 298 (61.4%) eyes (10 [17.8%] and 288 [67.1%] of aphakic and pseudophakic eyes, respectively) and best corrected vision ≥ 6/18 in 411 (84.7%) in all eyes. A presenting visual acuity less than 6/60 was found in 27 (6.3%) pseudophakic eyes. There was no significant difference in visual outcome based on age, sex, literacy, or institution. Uncorrected refractive error was the main cause (72.9%) of visual impairment in pseudophakic eyes. CONCLUSIONS: Visual acuity outcome after cataract surgery requires further improvement to meet World Health Organization standards, particularly improvement in preoperative biometry and refractive services. The same quality cataract surgery was provided in and equitably distributed throughout Lumbini Zone and Chitwan District independent of age, sex, literacy or location.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".