Objective Assessment of Microsurgery Competency—In Search of a Validated Tool
Bibliographic record
Abstract
Abstract Microsurgical skill acquisition is an integral component of training in plastic surgery. Current microsurgical training is based on the subjective Halstedian model. An ideal microsurgery assessment tool should be able to deconstruct all the subskills of microsurgery and assess them objectively and reliably. For our study, to analyze the feasibility, reliability, and validity of microsurgery skill assessment, a video-based objective structured assessment of technical skill tool was chosen. Two blinded experts evaluated 40 videos of six residents performing microsurgical anastomosis for arteriovenous fistula surgery. The generic Reznick's global rating score (GRS) and University of Western Ontario microsurgical skills acquisition/assessment (UWOMSA) instrument were used as checklists. Correlation coefficients of 0.75 to 0.80 (UWOMSA) and 0.71 to 0.77 (GRS) for interrater and intrarater reliability showed that the assessment tools were reliable. Convergent validity of the UWOMSA tool with the prevalidated GRS tool showed good agreement. The mean improvement of scores with years of residency was measured with analysis of variance. Both UWOMSA (p-value: 0.034) and GRS (p-value: 0.037) demonstrated significant improvement in scores from postgraduate year 1 (PGY1) to PGY2 and a less marked improvement from PGY2 to PGY3. We conclude that objective assessment of microsurgical skills in an actual clinical setting is feasible. Tools like UWOMSA are valid and reliable for microsurgery assessment and provide feedback to chart progression of learning. Acceptance and validation of such objective assessments will help to improve training and bring uniformity to microsurgery education.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".