Fat Necrosis in Single Perforator Deep Inferior Epigastric Artery and Superficial Epigastric Artery Perforator Free Flaps: A Prospective Randomized Study
Bibliographic record
Abstract
Background: The deep inferior epigastric perforator (DIEP) flap is the standard of care in autologous breast reconstruction. The superficial inferior epigastric artery perforator flap (SIEA) is an alternative reconstructive option, with the compromise of less donor-site morbidity but variable perfusion to subscarpal fat zones. Fat necrosis is a known complication from marginal perfusion variability. Volumetric analysis of fat necrosis has not been performed between the two reconstructive options, nor has the amount of flap necrosis following radiation. Our objective was to compare rates and volume of fat necrosis between single-perforator DIEP and SIEA flap techniques. Methods: A single-center, blinded, prospective cohort study of patients randomized between SIEA and DIEP breast reconstruction was conducted over 2 years (June 2011–July 2013). Inclusion criteria were women undergoing immediate reconstruction following mastectomy. Randomization protocols were strictly followed. Fat necrosis volumetric analysis was determined by an ultrasound-trained attending surgeon at 12 months postoperatively. Patient demographics and adjuvant/neoadjuvant cancer treatment were analyzed. Statistical analyses included Mann-Whitney U tests, chi square, and/or Fisher exact tests. P values of 0.05 or less were considered significant. Results: Fat necrosis was detected in 11 of 46 flaps (23.9%), with a median area of 17.9 cm2. There was no significant difference in prevalence of fat necrosis between the two flap types (P = 0.19). Postoperative radiation did not increase the prevalence (P = 0.30) or extent (P = 0.92) of fat necrosis. Conclusion: Single-perforator DIEP and SIEA flaps have comparable rates of fat necrosis. Postoperative radiation did not result in increased prevalence or extent of fat necrosis.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".