Demographic trends of patients undergoing ophthalmic surgery in Ontario, Canada: a population-based study
Bibliographic record
Abstract
OBJECTIVE: In this study, we investigated the demographic trends of patients undergoing ophthalmic surgeries based on geographic region, priority level, and sex. METHODS AND ANALYSIS: This population-based retrospective cohort study used the Ontario Health Wait Times Information System (WTIS) database from 2010 to 2021. The WTIS contains non-emergent surgical case volume and wait time data for 14 different regions, three priority levels (high, medium and low) and six ophthalmic subspecialty procedures. RESULTS: Over the study period, on average 83 783 women and 65 555 men underwent ophthalmic surgery annually in Ontario. Overall, women waited an aggregate mean of 4.9 days longer than men to undergo surgery, and this disparity persisted across all geographic and priority stratifications. The average age at the time of surgery has been increasing slowly at a rate of 0.02 years/year (95% CI 0.00 to 0.05), with women being 0.6 years older than men overall. CONCLUSION: These findings indicate that women have consistently longer wait times than men. The results of this study may be a sign of systemic sex-based differences that could be affecting women who need to be further explored for health equity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".