Donor site aesthetics and morbidity after DIEP flap breast reconstruction—A retrospective multicenter study
Bibliographic record
Abstract
The deep inferior epigastric artery perforator flap (DIEP) has gained widespread popularity in autologous breast reconstruction due to its natural aesthetic results and muscle-sparing design. However, donor site results regarding aesthetic outcome are often less favorable. We therefore aimed to identify crucial factors that might increase the risk for abdominal bulging and an impaired aesthetic appearance. We conducted a multicenter study evaluating all patients receiving autologous breast reconstruction using a DIEP flap between 2013 and 2017. Medical records were analyzed with special attention to flap technique, number of perforators, localization of perforator, and donor site complications. In addition, the aesthetic appearance of the abdominal donor site was evaluated by blinded clinicians at one-year follow-up. A total of 242 patients underwent DIEP flap breast reconstruction. Abdominal bulging occurred in 7%. Further subgroup analysis revealed a significant correlation between abdominal bulging and two or more perforators (P = .003), the use of lateral row perforators (P = .009), and a higher BMI (P = .002). Obesity (P = .003) and higher patient's age (P = .003) could be identified as risk factors for an undesirable appearance of the donor site. We recommend the use of a medial-row single perforator whenever possible in order to optimize donor site morbidity and decrease the risk of abdominal bulging. Proper patient selection and careful donor site closure following a standardized approach should be performed to limit the risk of aesthetically undesirable results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".