Prevalence of eye disease and visual impairment in Île de la Gonave, Haïti
Bibliographic record
Abstract
Epidemiological data describing the prevalence of blindness and visual impairment in Haiti are sparse. The Haitian National Committee for the Prevention of Blindness (CNPC) estimates the prevalence of blindness at 1 %. Other regional data estimate moderate and severe visual impairment at 5% and 22%, respectively. IRIS Mundial (IM) is a non-governmental organization collaborating with the CNPC to develop eye care infrastructure in Haiti. To estimate the prevalence and causes of blindness and visual impairment on the Haitian island of Gonâve, to assist in planning of relevant eye care infrastructure. Results from eye exams carried out by a team from IM in January 2013 have been compiled and analyzed. In all, 1724 patients were examined (38% men, 62% women). In the best eye, 87% of patients had visual acuity, 6% had moderate visual impairment, and 7% had severe visual impairment. Moreover, 1% of patients had high myopia, 1% high hyperopia, 1% high astigmatism, and 32% were presbyopic. Clinically significant binocular cataracts were found in 1.5 % of patients, while 2 % were diagnosed with probable glaucoma. Our data give a glimpse of the prevalence of visual impairment and ocular disease on Gonâve Island in Haiti. Uncorrected refractive error, cataracts, and glaucoma are confirmed as prevalent conditions in this population and their presence should guide the planning of relevant eye care interventions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".