Distribution gaps in cataract surgery care and impact on seniors across Ontario
Bibliographic record
Abstract
OBJECTIVE: To assess recent cataract service delivery across communities of all sizes in Ontario. DESIGN: Retrospective analysis of health records. PARTICIPANTS: All Ontario Health Insurance Plan users. METHODS: Raw physician Ontario Health Insurance Plan claims data for cataract surgery (E140A, E214A) from April 1, 2009, to March 31, 2014, were extracted from the Ontario Ministry of Health and Long-Term Care (MOHLTC) IntelliHealth database. Cataract surgery claims data were sorted by sex, by age, and by Ontario's 444 municipalities based on patient residence. Cataract surgery distribution was examined by population centre: Large Urban (≥100 000 persons), Medium (30 000-99 999 persons), Small (1000-29 999 persons), and Rural (<1000 persons) as defined by Statistics Canada. Wait times were extracted from the MOHLTC wait times database. Cataract surgery rate (CSR), defined as the number of cataract surgeries performed per million, was calculated. RESULTS: Cataract surgery volumes remained unchanged from 2010 to 2014. Mean patient age was 71.6 ± 10 years. Patients lived in large urban (63%), medium (15%), small (21%), and rural (0.6%) communities. Mean wait times increased by 28% to 68.5 days, and 90th percentile wait times increased by 44% to 154.3 days. A reduction in CSR was observed among seniors aged 65-74 years (-10%) and 75+ years (-16%). Rural communities showed the largest decline (-19%). Among seniors aged ≥75 years, CSR declined the most for those living in rural communities (-25%). CONCLUSIONS: Adjusting the current government policy of zero-growth in cataract surgery volumes will support growing demands for cataract care in our aging population.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".