A New, Validated Instrument to Evaluate Competency in Microsurgery: The University of Western Ontario Microsurgical Skills Acquisition/Assessment Instrument [Outcomes Article]
Bibliographic record
Abstract
BACKGROUND: The authors present a model for microsurgery learning as well as a validated instrument to evaluate microsurgical competency. METHODS: Novice microsurgeons participated in three 3-hour sessions wherein they completed a number of increasingly complex, standardized microsurgical tasks. Performance was recorded and graded using a newly developed University of Western Ontario Microsurgery Skills Acquisition/Assessment (UWOMSA) instrument. The knot-tying and anastomosis modules contained three categories with five-point Likert scales. Each learner's performance was assessed by two blinded surgeons. Reznick's validated global rating scale for operative performance was utilized to establish criterion validity. Within-scale scores were compared via intraclass correlation and between-scale scores with Pearson correlation coefficient. Linear regression was used to evaluate the effect of various predictors on UWOMSA scores. RESULTS: Thirty-seven videos (9.6 hours) were reviewed, including 20 knot-tying sessions and 17 anastomoses. Interrater reliability of UWOMSA was high, with an intraclass correlation coefficient of 0.75 (0.57, 0.87). The intraclass correlation of the global rating scale was 0.79 (0.62, 0.89). Intrarater reliability of the UWOMSA was also high, with an intraclass correlation of 0.69 (0.48, 0.83). The intraclass correlation of the global rating scale was 0.69 (0.47, 0.84). Measures of criterion validity demonstrated strong agreement between UWOMSA and the global rating scale (Pearson correlation coefficient, 0.96; p < 0.001). Measures of construct validity demonstrated that higher scores on the UWOMSA were associated with faster knot tying (p < 0.0001) and higher postgraduate year level (p = 0.05). CONCLUSIONS: The UWOMSA instrument performed well in terms of reliability and validity. Further study is planned to assess the instrument's ability to predict microsurgical skills translation to the clinical setting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".