Evaluating the reliability of surgical assessment methods in an orthopedic residency program
Bibliographic record
Abstract
BACKGROUND: Orthopedic surgical education in Canada has seen major change in the last 15 years. Work hour restrictions and external influence have led to new approaches for surgical training. With a change toward competency-based educational models under the CanMEDS headings there is a need to ensure the validity of modern assessment methods. Our objective was to evaluate the reliability of a currently used surgical skill assessment tool within an orthopedic residency program, as measured by the Surgical Encounters Form. METHODS: A surgical assessment tool has previously been created at our institution that comprises 15 items spanning 4 of the CanMEDS competencies. Results were blinded to the primary investigator and coded by a third party. The assessments were collected, and we measured percent agreement using Cronbach's α and Fleiss κ. RESULTS: Over a 5-month period 11 staff members assessed 10 residents. Eighty-eight assessments were completed in total. Weighted percent agreement was 90.9%. Cronbach's α averaged 0.865 for the medical expert role, 0.920 for technical skills, 0.934 for the communicator role, 1.00 for the collaborator role and 1.00 for the health advocate role. The mean Fleiss κ score was 0.147 (95% confidence interval -0.071 to 0.364), demonstrating low interrater reliability. CONCLUSION: Despite the development of a validated assessment tool to evaluate surgical skills acquisition, interrater reliability results suggest low levels of agreement among assessors.
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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 | 0.072 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".