Gender and use of cataract surgical services in developing countries.
Bibliographic record
Abstract
OBJECTIVE: To determine, from the existing literature, cataract surgical coverage rates by sex and the proportion of cataract blindness that could be eliminated if women and men had equal access to cataract surgical services. METHOD: Methodologically sound population-based cataract surveys from developing countries were identified through a literature search. Cataract surgical coverage rates were extracted from the surveys and rates for women were compared to those for men. Peto odds ratios were calculated for each survey and a meta-analysis of the surveys was performed. FINDINGS: From a literature review and meta-analysis of cataract surveys in developing countries, we found that the cataract surgical coverage rate was 1.2-1.7 times higher for males than for females. For females, the odds ratio of having surgery, compared to males, was 0.67 (95% confidence interval (CI): 0.60- 0.74). Despite their lower coverage rate, females accounted for approximately 63% of all cataract cases in the study populations, and if they received surgery at the same rates as males, the prevalence of cataract blindness would be reduced by a median of 12.5% (range 4-21%). CONCLUSION: Closing the gender gap could thus significantly decrease the prevalence of cataract blindness, and gender-sensitive intervention programmes are needed to improve cataract surgical coverage among females.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".