A multicentric cross-sectional study measuring the equity of cataract surgical services in three high-volume eyecare organizations in North India: Equitable cataract surgical rate as a new indicator
Bibliographic record
Abstract
PURPOSE: Cataract remains the leading cause of blindness and visual impairment in most low-and middle-income countries, with the greatest burden borne by women. To achieve Global Action Plan targets, cataract programs must target people, especially women, with maximum need. This study examines whether cataract surgical programs in three major north Indian eyecare institutions are equitable and describes a refined indicator for reporting equity. METHODS: Retrospective one-year cross-sectional study of cataract surgery utilization using routine administrative data from three north Indian eyecare institutions. Patient data were categorized by paying category, sex, and preoperative visual acuity. Comparisons were made between payment categories and sexes. RESULTS: Out of the total number of patients operated, 86,230 were in the non-paying category and 56,738 in the paying category. Overall, 8.2% were blind, 21.1% were severely visual impaired (SVI) or worse, and 86.1% were moderate visual impaired (MVI) or worse. Non-paying patients had a significantly higher proportion of poorer visual categories compared to paying patients [(blind, 9.7% vs. 5.8%; SVI or worse, 24.6% vs. 15.8%; and MVI or worse, 89.1% vs. 81.6%, respectively, (P < 0.001)]. Women had significantly higher proportion of poorer visual categories than men [(blind, 8.9% vs. 7.4%, SVI or worse, 21.9% vs. 20.3% and MVI or worse 87.6 vs. 84.7%) (P < 0.001)]. CONCLUSION: The institutions primarily provided surgery to patients with maximum need: too poor to pay, low visual acuity, and women. Similar data from all service providers of a region can help estimate the proposed "equitable cataract surgical rate": the proportion of patients operated with maximum need among those operated in a year. This can be used for targeting people in need.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".