Intraoperative complication rates in cataract surgery performed by ophthalmology resident trainees compared to staff surgeons in a Canadian academic center
Bibliographic record
Abstract
PURPOSE: To compare the intraoperative complication rates in cataract surgery performed by resident trainees and staff ophthalmologists. SETTING: Kensington Eye Institute, University of Toronto, Toronto, Canada. DESIGN: Prospective case series. METHODS: This study included 8738 consecutive cases of primary phacoemulsification cataract surgery performed by staff surgeons and resident trainees from January to December 2016. There were no exclusion criteria. Data collected included the level of resident training, case complexity, degree of resident involvement, and intraoperative complications. Primary outcome measures included intraoperative complication rates and level of complexity of cataract surgeries performed by resident trainees and staff surgeons. RESULTS: Resident trainees were involved in 44% of surgeries. Of those, 82% were completed in their entirety by a resident and 18% were performed by both the staff surgeon and resident. Staff surgeons performed 56% of all surgeries without resident involvement. Sixty-seven percent of surgeries were simple and 33% were complex, with small pupil or intraoperative floppy-iris syndrome being the most common reason for complex cases. For simple cases, there was no difference in the overall complication rates (1.7% and 2.0%; P = .52), posterior capsule rupture rates (0.9% and 0.8%; P = .76), or vitreous loss rates (0.4% and 0.2%; P = .08) between staff and residents, respectively. CONCLUSION: There were no differences in complication rates between the two groups.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".