Association of rural or urban status, region, and institution type with cataract surgery wait times in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: We examined the cataract surgery wait time (WT) disparities across Ontario regions and institution types using a data-driven, population-level, and objective method based on billing codes. METHODS: We identified 1,138,532 adults who underwent cataract surgery between 2005-2019. WT from referral to initial surgeon visit (WT1) and from surgery decision to first eye surgery (WT2) were compared between rural (population <10,000) and urban settings, LHINs, and institution types (acute care, ambulatory, or other). RESULTS: Median WT1 and WT2 were 70 and 81 days for rural patients and 67 and 76 days for urban patients. Overall, 18.8% and 20.8% of rural patients exceeded WT1 and WT2 provincial guidelines of 182 days, compared to 20.6% and 20.7% of urban patients. Median WT1 and WT2 were 69 and 78 days for surgeries done in acute care (n=517,511), and 73 and 84 for surgeries in ambulatory care (n=270,648) respectively. The percentage exceeding provincial guidelines in both groups ranged from 20.2% to 21.8%. Across LHINs, WT1 ranged from 36 (Erie St. Clair) to 85 days (North Simcoe Muskoka), and WT2 ranged from 44 (Erie St. Clair) to 110 (Champlain). The percentage of patients with WT1 and WT2 exceeding provincial guidelines ranged from 11.4% (Erie St. Clair) to 24.7% (Toronto Central), and from 10.7% (Erie St. Clair) to 30.4% (Champlain) respectively. INTERPRETATION: Though WTs were comparable between institution type, urban, and rural settings, northern LHINs experienced higher WTs than southern LHINs. More data-driven studies are needed to inform health care policies aimed at addressing access to cataract surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".