Prevalence of blindness in Western Australia: a population study using capture and recapture techniques
Bibliographic record
Abstract
AIM: To determine the prevalence of blinding eye disease in Western Australia using a capture and recapture methodology. METHODS: Three independent lists of residents of Western Australia who were also legally blind were collated during the capture periods in 2008-9. The first list was obtained from the state-wide blind register. A second list comprised patients routinely attending hospital outpatient eye clinics over a 6-month period in 2008. The third list was patients attending ophthalmologists' routine clinical appointments over a 6-week period in 2009. Lists were compared to identify those individuals who were captured on each list and those who were recaptured by subsequent lists. Log-linear models were used to calculate the best fit and estimate the prevalence of blindness in the Western Australian population and extrapolated to a national prevalence of blindness in Australia. RESULTS: 1771 legally blind people were identified on three separate lists. The best estimate of the prevalence of blindness in Western Australia was 3384 (95% CI 2947 to 3983) or 0.15% of the population of 2.25 million. Extrapolating to the national population (21.87 million) gave a prevalence of legal blindness of approximately 32,892 or 0.15%. CONCLUSION: Capture-recapture techniques can be used to determine the prevalence of blindness in whole populations. The calculated prevalence of blindness suggested that up to 30% of legally blind people may not be receiving available financial support and up to 60% were not accessing rehabilitation services.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".