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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".