Development of a certification examination for orthopedic sports medicine fellows
Bibliographic record
Abstract
Background: The purpose of this study was to develop a multifaceted examination to assess the competence of fellows following completion of a sports medicine fellowship. Methods: Orthopedic sports medicine fellows over 2 academic years were invited to participate in the study. Clinical skills were evaluated with objective structured clinical examinations, multiple-choice question examinations, an in-training evaluation report and a surgical logbook. Fellows’ performance of 3 technical procedures was assessed both intraoperatively and on cadavers: anterior cruciate ligament reconstruction (ACLR), arthroscopic rotator cuff repair (RCR) and arthroscopic shoulder Bankart repair. Technical procedural skills were assessed using previously validated task-specific checklists and the Arthroscopic Surgical Skill Evaluation Tool (ASSET) global rating scale. Results: Over 2 years, 12 fellows were assessed. The Cronbach α for the technical assessments was greater than 0.8, and the interrater reliability for the cadaveric assessments was greater than 0.78, indicating satisfactory reliability. When assessed in the operating room, all fellows were determined to have achieved a minimal level of competence in the 3 surgical procedures, with the exception of 1 fellow who was not able achieve competence in ACLR. When their performance on cadaveric specimens was assessed, 2 of 12 (17%) fellows were not able to demonstrate a minimal level of competence in ACLR, 2 of 10 (20%) were not able to demonstrate a minimal level of competence for RCR and 3 of 10 (30%) were not able to demonstrate a minimal level of competence for Bankart repair. Conclusion: There was a disparity between fellows’ performance in the operating room and their performance in the high-fidelity cadaveric setting, suggesting that technical performance in the operating room may not be the most appropriate measure for assessment of fellows’ competence.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".