Error-Based Teaching Approach Decreases Vessel Anastomosis Errors: A Pilot Study
Bibliographic record
Abstract
Abstract Background Microsurgical anastomosis of vessels is a challenging skill that surgical residents should practice on models before attempting in the clinical setting. These skills are often taught using synthetic materials, animal tissue, or live animal models. With increasing constraints on surgical resident's time, it is important to maximize efficiency of microsurgical training. The purpose of this study is to determine if teaching surgical residents about common vessel anastomosis errors decreases the total number of suture errors during a 4-day training course. Methods Plastic surgery residents (R1–R3) were randomly assigned to receive additional teaching focused on either common microsurgical errors or traditional microsurgical manuals. The residents then performed anastomosis on rat femoral arteries in which the total number of sutures and errors were recorded by staff microsurgeons who were blinded to the intervention. Results Residents who received teaching on common microsurgical errors performed a total of 73 sutures of which 12 were errors. The control group who studied using traditional microsurgical manuals performed a total of 125 sutures of which 38 were errors. There was a statistically significant decrease in the total number of suture errors (Fisher's exact test; p-value = 0.04) and in the number of partial depth bite errors (Fisher's exact test p-value = 0.03). Conclusion Teaching surgical residents about common vessel anastomosis errors decreased the total number of errors when compared with traditional education methods using microsurgery manuals. Partial depth bite errors were also decreased through error-based teaching.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".