The Impact of the COVID-19 Pandemic on Wait-Times for Ophthalmic Surgery in Ontario, Canada: A Population-Based Study
Bibliographic record
Abstract
Objective: To investigate the effects of the COVID-19 pandemic on case volumes and wait-times for ophthalmic surgery in Ontario, Canada. Design: Population-based retrospective cohort study. Participants: Patients undergoing ophthalmic surgery in Ontario, Canada, from 2010 to 2021, collected from the Ontario Health Wait Times Information System (WTIS) database. Methods: The WTIS contains non-emergent surgical case volume and wait-time data for six ophthalmic subspecialty surgery types, three priority levels (low, medium, high) and 14 different regions in Ontario. Case volume and wait-times were compared between the COVID-19 pandemic (2020-2021) and the preceding time period (2010-2019) across all stratifications. Results: There was a significant decrease in case volumes and significant increase in wait-times across geographic regions, priority levels, and subspecialty surgeries from the pre-pandemic to pandemic period. Moreover, COVID-19 exacerbated pre-existing wait-time disparities between sexes, with females waiting 4.1 days longer than males overall to receive surgery in 2010-2019 compared to waiting 8.8 days longer in 2020-2021 (117% increase). Conclusion: These findings highlight the impact of the COVID-19 pandemic on ophthalmic surgical wait times in Ontario. Cataract, strabismus and oculoplastic surgeries, the Waterloo Wellington, Central, and South East regions of Ontario, and those with female sex had the greatest relative increases in wait-times during the pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".