Evidence-based cataract surgery teaching milestones: guide to evaluate resident achievement
Bibliographic record
Abstract
PURPOSE: To develop evidence-based milestones for cataract surgery teaching and identify performance indicators. SETTING: Royal Alexandra Hospital, Edmonton, Alberta, Canada. DESIGN: Retrospective cohort study. METHODS: Operative records from a single surgeon were reviewed for resident participation when learning cataract surgery over a 14-year period. Time to complete a resident's first complete case was the primary outcome. Secondary outcomes included mean time to perform each categorical step of the procedure, number of cases participated in, rate of participation, complex case involvement, and complications. Strong resident performance was defined as time to first complete a case 1 SD quicker than mean performance; weak performance was the opposite. RESULTS: Residents (n = 13) performed beginner steps for 3.1 ± 3.2 months and intermediate steps until month 4.3 ± 3.3, and by month, 5.1 ± 3.4 residents were able to do complete cases. Time to perform a complete case increased with lower case participation (P = .02); mean proportion of complex cases that a resident participated in was 7.9% (n = 17.6 ± 10.0); less than 1% of resident cases resulted in posterior capsular rupture (PCR; n = 1.4 ± 1.3 cases). Based on these data, weaker achievement was defined as failure to achieve beginner-step competency by month 6.3, intermediate step competency by month 7.6, or inability to perform a complete case by month 8.5. In this dataset, 23.1% of residents (n = 3) met this definition. CONCLUSIONS: Residents who train with multiple teachers during a focused cataract surgery rotation can perform complete cases after a mean of 5.1 ± 3.4 months with a low PCR rate.
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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 | 0.030 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".