Gender Issues in a Cataract Surgical Population in South India
Bibliographic record
Abstract
PURPOSE: To investigate patterns and characteristics of men and women who used different cataract surgery payment streams in a South Indian hospital. METHODS: We randomly sampled patients with age-related cataract aged 40 years and over from three routine cataract surgical service streams: walk-in paying, walk-in subsidized and free camp. Presenting visual acuity (VA) and cataract surgical details were obtained from routine hospital records. Demographic and socioeconomic factors were collected from patient interviews. Multiple logistic regression was used to investigate factors associated with use of different streams with walk-in paying as the reference group. RESULTS: There were 7076 eligible admissions (3742 women and 3334 men). Proportionately more women than men attended the walk-in subsidized (56%) or free camp sections (55%) compared to the walk-in paying stream (42%, odds ratio, OR, 1.40 95% confidence interval, CI, 1.25-1.57 and OR 1.33 95% CI 1.19-1.49, respectively). After adjustment for socioeconomic factors (illiteracy, not being in paid work), rural residence and poor presenting VA, OR for women compared to men for the walk-in subsided stream was 1.02, (95% CI 0.87-1.18) and for the free camp 0.94 (95% CI 0.80-1.11). CONCLUSION: Our results indicate that women are underrepresented in the paying section, reflecting their poorer socioeconomic and educational statuses.
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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) | 0.002 | 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".