A retrospective population-based analysis of wait times for cataract surgery in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Current methods used to estimate surgical wait times in Ontario may be subject to inconsistencies and inaccuracies. In this population-level study, we aimed to estimate cataract surgery wait times in Ontario using a novel, objective and data-driven method. METHODS: We identified adults who underwent cataract surgery between 2005 and 2019 in Ontario, using administrative records. Wait time 1 represented the number of days from referral to initial visit with the surgeon, and wait time 2 represented the number of days from the decision for surgery until the first eye surgery date. In the primary analysis, a ranking method prioritized referrals from optometrists, followed by ophthalmologists and family physicians. RESULTS: The cohort consisted of 1 138 532 people with mostly female patients (57.4%) and those aged 65 years and older (79.0%). In the primary analysis, the median was 67 days for wait time 1 (interquartile range [IQR] 29-147). There was a median of 77 days for wait time 2 (IQR 37-155). Overall, the following proportions of patients waited less than 3, 6 and 12 months: 54.1%, 78.5% and 91.7%, respectively. For wait time 2, the proportions of patients who waited less than 3, 6 and 12 months were 49.5%, 77.1% and 93.3%, respectively. In total, 19.3% of patients did not meet the provincial target for wait time 1, 20.5% did not meet the target for wait time 2 and 35.0% did not meet the target for wait times 1 or 2. INTERPRETATION: Administrative health services data can be used to estimate cataract surgery wait times. With this method, 35.0% of patients in 2005-2019 did not receive initial consultation or surgery within the provincial wait time target.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".