Twinning Initiative: A 7-phase Multidisciplinary Mentorship Model to Implement Deep Inferior Epigastric Perforator Flap Breast Reconstruction in New Centers
Bibliographic record
Abstract
Background: Introducing complex microsurgical techniques in resource-limited settings is a challenge. The "twinning initiative" provides a structured and phased mentorship between experienced and recipient centers, eventually improving local autonomy and reducing complications over time. The goal of the study was to assess the feasibility and outcomes of the twinning initiative in teaching deep inferior epigastric perforator (DIEP) breast reconstruction at the University Hospital Center of Martinique. Methods: The model used a 7-phase approach: networking, academic twinning, on-site visits, and increasing local team involvement. The senior microsurgeon performed 100% of critical tasks initially, with the local team gradually taking more responsibility. Data on demographics, intraoperative details, complications, and outcomes were collected. Results: This observational study included 14 patients undergoing DIEP reconstruction at the University Hospital Center of Martinique from February 2022 to June 2024. The local team's surgical autonomy grew, with several cases managed independently. Initial complications included flap failures and equipment issues, but these decreased over time, with no flap failures by the second year. Flap failure occurred in 14% of the cases, and 29% of patients needed reoperation. As local surgeons became more confident, problems decreased, and the inclusion of anesthesiologists helped reduce complications. Conclusions: The twinning initiative effectively transferred the DIEP technique, increased surgical autonomy, and improved outcomes. It shows the importance of a structured team approach for transferring the microsurgery technique. However, a larger sample size is required in further research to confirm the applicability of the model to other procedures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".