Evidence-based Algorithms for Free Deep Inferior Epigastric Perforator Flap Salvage in Autologous Breast Reconstruction
Bibliographic record
Abstract
Background: Breast reconstruction with the deep inferior epigastric perforator (DIEP) free flap has become the gold standard for autologous breast reconstruction. Flap take-back to the operating room (OR) is an uncommon but difficult situation, requiring prompt and accessible resources. We conducted a literature review and independent expert review to inform evidence-based perioperative algorithms in the event of DIEP flap compromise. Methods: A review of the literature was conducted, including MEDLINE, Embase, Google Scholar, and Cochrane Controlled Register of Trials. Publications examining free flap re-exploration in breast reconstruction were used to inform evidence-based clinical algorithms. The algorithms then underwent expert review and revisions from 6 international experts in microsurgery. Results: Three evidence-based management algorithms were created. The first algorithm outlines perioperative management strategies to optimize patient care and prompt return to the OR. Nonconstricting flap inset after take-back, salvage medical strategies and postoperative management following flap failure were additionally included. Algorithms 2 (venous congestion) and 3 (vascular thrombosis) provide specific intraoperative strategies surrounding mechanical decompression, pedicle exposure, assessment and extraction of thrombosis, identification and use of alternative recipient vessels, and the usage of intraoperative thrombolytics. Conclusions: A coherent and stepwise approach to DIEP flap compromise in breast reconstruction was developed. These expert-reviewed algorithms provide an approachable and evidence-based structure to support return to the OR and serve as readily available resources.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".