Feedback and Assessment Methods in Microsurgery Education: A Scoping Review
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Bibliographic record
Abstract
With distinctive instrumentation, challenges, and training, the unique nature of microsurgery necessitates the provision of feedback and assessment for trainees. The uncertain applicability of feedback or assessment methods may lead to poor trainee satisfaction and operative outcomes. We conducted a scoping review of the feedback and assessment methods in microsurgery.The Medline, EMBASE, ERIC, and Web of Science databases were searched for studies discussing feedback and/or assessment of microsurgery trainees. Study characteristics, feedback methods, assessment methods, and all other relevant data were extracted. The Medical Education Research Study Quality Instrument (MERSQI) was used to critically appraise the quantitative studies.From 2,440 articles, 99 were included. Sixty-five percent of articles were published since 2015. Plastic surgery, neurosurgery, and ophthalmology were the most common surgical specialties. Ninety percent of articles discussed exclusively assessment methods, with only 10% discussing both feedback and assessment. Microvascular anastomosis was the most common task (55%), with ex vivo synthetic, (20%) chicken (16%), and rat models (11%) being widely used. Global rating scales (GRSs) providing holistic evaluation based on multiple competency domains were the most common assessment methods (73%), followed by checklists (23%), and device-derived metrics (21%). Parameters included suture placement (53.5%), dexterity (50.5%), and tissue handling (48.5%). Real-time verbal, one-to-one feedback was the most common method among relevant studies (80%), while delayed written video review (20%) was also used. No structured feedback methods were used.This review identified a variety of feedback and assessment methods specific to microsurgery. GRSs continue to be popular; however, with increasing accessibility, device-derived metrics continue to increase in prevalence. A juxtaposition between named, structured, and validated assessment methods and informal feedback methods was evident. Particularly, the lack of standardized feedback methods may act as a barrier to the implementation of feedback across microsurgical 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) | 0.004 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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 it