Objective Assessment of Microsurgery Competency—In Search of a Validated Tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it