Gender and use of cataract surgical services in developing countries.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it