Blindness and cataract surgical services in Atsinanana region, Madagascar
Bibliographic record
Abstract
PURPOSE: To assess the prevalence and causes of avoidable blindness in Atsinanana Region, Madagascar, with the Rapid Assessment of Avoidable Blindness (RAAB) survey. We analyzed the hospital records to supplement the findings for public health care planning. MATERIALS AND METHODS: Only villages within a two-hour walk from a road, about half of the population of Atsinanana was included. Seventy-two villages were selected by population-proportional-to-size sampling. In each village, compact segment sampling was used to select 50 people over age 50 for eye examination using standard RAAB methods. Records at the two hospitals providing cataract surgery in the region were analyzed for information on patients who underwent cataract surgery in 2010. Cataract incidence rate and target cataract surgery rate (CSR) was modeled from age-specific prevalence of cataract. RESULTS: The participation rate was 87% and the sample prevalence of blindness was 1.96%. Cataract was responsible for 64% and 85.7% of blindness and severe visual impairment, respectively. Visual impairment was due to cataract (69.4%) and refractive error (14.1%). There was a strong positive correlation between cataract surgical rate by district and the proportion of people living within 2 hours of a road. There were marked differences in the profiles of the cataract patients at the two facilities. The estimated incidence of cataract at the 6/18 level was 2.4 eyes per 100 people over age 50 per year. CONCLUSIONS: Although the survey included only people with reasonable access, the main cause of visual impairment was still cataract. The incidence of cataract is such that it ought to be possible to eliminate it as a cause of visual impairment, but changes in service delivery at hospitals and strategies to improve access will be necessary for this change.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".