Use of an Objective Structured Assessment of Technical Skill After a Sports Medicine Rotation
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine if the use of an Objective Structured Assessment of Technical skill (OSATS), using dry models, would be a valid method of assessing residents' ability to perform sports medicine procedures after training in a competency-based model. METHODS: Over 18 months, 27 residents (19 junior [postgraduate year (PGY) 1-3] and 8 senior [PGY 4-5]) sat the OSATS after their rotation, in addition to 14 sports medicine staff and fellows. Each resident was provided a list of 10 procedures in which they were expected to show competence. At the end of the rotation, each resident undertook an OSATS composed of 6 stations sampled from the 10 procedures using dry models-faculty used the Arthroscopic Surgical Skill Evaluation Tool (ASSET), task-specific checklists, as well as an overall 5-point global rating scale (GRS) to score each resident. Each procedure was videotaped for blinded review. RESULTS: The overall reliability of the OSATS (0.9) and the inter-rater reliability (0.9) were both high. A significant difference by year in training was seen for the overall GRS, the total ASSET score, and the total checklist score, as well as for each technical procedure (P < .001). Further analysis revealed a significant difference in the total ASSET score between junior (mean 18.4, 95% confidence interval [CI] 16.8 to 19.9) and senior residents (24.2, 95% CI 22.7 to 25.6), senior residents and fellows (30.1, 95% CI 28.2 to 31.9), as well as between fellows and faculty (37, 95% CI 36.1 to 27.8) (P < .05). CONCLUSIONS: The results of this study show that an OSATS using dry models shows evidence of validity when used to assess performance of technical procedures after a sports medicine rotation. However, junior residents were not able to perform as well as senior residents, suggesting that overall surgical experience is as important as intensive teaching. CLINICAL RELEVANCE: As postgraduate medical training shifts to a competency-based model, methods of assessing performance of technical procedures become necessary.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".