Teaching simulated arthroscopic Bankart repair: residents’ assessment at the Annual Shoulder Course
Bibliographic record
Abstract
Background: This study’s aim was to evaluate the performance of senior orthopedic residents during simulated arthroscopic anterior stabilization (Bankart repair) before and after a national shoulder review course. Methods: Participants were assessed before and after the Annual Shoulder Review Course over a 3-day period, using a multiple-choice examination and surgery performance assessment. The surgical evaluation was completed by fellowship-trained surgeons using a standardized procedure checklist and a global rating scale. All Canadian senior orthopedic residents were invited to participate in the course. Results: The 57 participants showed improvement following the course. The written knowledge evaluation mean score increased, and all 3 surgical performance measurements improved: surgical task time improved from 4:40 min to 2:53 min (p < 0.001), surgical technique evaluation increased from 56% to 67% after the procedure checklist (p < 0.001), and anchor placement improved for all 3 aspects. Anchor entry point was the sole measure not to improve enough to reach statistical significance (p = 0.37). Conclusion: Our data support the inclusion of dry model surgical simulation as part of a surgical skills course for both training and assessment of orthopedic surgery residents.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".