Surgeon-Reported Needs for Improved Training in Identifying and Managing Free Flap Compromise
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
Background This study examined the need for improved training in the identification and management of free flap (FF) compromise and assessed a potential role for simulated scenario training. Methods Online needs assessment surveys were completed by plastic surgeons and a subsample with expertise in microsurgery education participated in focus groups. Data were analyzed using descriptive statistics and mixed qualitative methods. Results In this study, 77 surgeons completed surveys and 11 experts participated in one of two focus groups. Forty-nine (64%) participants were educators, 65 and 45% of which reported having an insufficient volume of FF cases to adequately teach the management and identification of compromise, respectively. Forty-three percent of educators felt that graduating residents are not adequately prepared to manage FF compromise independently. Exposure to normal and abnormal FF cases was felt to be critical for effective training by focus group participants. Experts identified low failure rates, communication issues, and challenging teaching conditions as current barriers to training. Most educators (74%) felt that simulated scenario training would be “very useful” or “extremely useful” to current residents. Focus groups highlighted the need for a widely accepted algorithm for re-exploration and salvage on which to base the development of a training adjunct consisting of simulated scenarios. Conclusion Trainee exposure to FF compromise is inadequate in existing plastic surgery programs. Early exposure, high case volume, and a standardized algorithmic approach to management with a focus on decision making may improve training. Simulated scenario training may be valuable in addressing current barriers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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