Microvascular Anastomosis in Practice: A Pilot Study on Microsurgical Training Efficiency
Bibliographic record
Abstract
Background: Microsurgery is a demanding surgical field requiring precision and extensive training. There is a continued need for standardized training models to improve skill acquisition and efficiency in microvascular anastomosis. Objectives: This study aimed to assess whether a standardized microsurgery training protocol, focusing on technique-specific objectives, improves performance among beginner trainees. Material and Methods: A three-month, non-randomized cohort study was conducted with entry-level plastic surgery residents. Participants were assigned to either a control group, practicing without structured guidance, or a test group, using a predefined microsurgery curriculum. Skill performance was measured at baseline, three weeks, and three months using a modified University of Western Ontario Microsurgical Skills Assessment (UWOMSA) tool. Results: While both groups improved over time, the test group demonstrated significantly greater improvement at the three-month mark (mean score: 59 vs. 38; p = 0.00027). The structured training model promoted more consistent progress and superior microsurgical technique. Conclusions: A standardized training protocol significantly enhances microsurgical proficiency over time. These findings suggest value in structured, low-cost training models for microsurgical education. Limitations include the small sample size, use of non-living models, and a non-randomized design.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".