Effect of cataract surgery volume constraints on recently graduated ophthalmologists: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Across Canada, graduates from several medical and surgical specialties have recently had difficulty securing practice opportunities, especially in specialties dependent on limited resources such as ophthalmology. We aimed to investigate whether resource constraints in the health care system have a greater impact on the volume of cataract surgery performed by recent graduates than on established physicians. METHODS: We used population-based administrative data from Ontario for the period Jan. 1, 1994, to June 30, 2013, to compare health services provided by recent graduates and established ophthalmologists. The primary outcome was volume of cataract surgery, a resource-intensive service for which volume is controlled by the province. RESULTS: When cataract surgery volume in Ontario entered a period of government-mandated zero growth in 2007, the mean number of cataract operations performed by recent graduates dropped significantly (-46.37 operations/quarter, 95% confidence interval [CI] -62.73 to -30.00 operations/quarter), whereas the mean rate for established ophthalmologists remained stable (+5.89 operations/quarter, 95% CI 95% CI -1.47 to +13.24 operations/quarter). Decreases in service provision among recent graduates did not occur for services without volume control. The proportion of recent graduates providing exclusively cataract surgery increased over the study period, and recent graduates in this group were 5.24 times (95% CI 2.15 to 12.76 times) more likely to fall within the lowest quartile for cataract surgical volume during the period of zero growth in provincial cataract volume (2007-2013) than in the preceding period (1996-2006). INTERPRETATION: Recent ophthalmology graduates performed many fewer cataract surgery procedures after volume controls were implemented in Ontario. Integrated initiatives involving multiple stakeholders are needed to address the issues facing recently graduated physicians in Canada.
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How this classification was reachedexpand
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.006 | 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 teacher head, 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".