Prevalence of blindness in Western Australia: a population study using capture and recapture techniques
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
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.
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 | 0.001 | 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