Comparison of Outcomes following Autologous Breast Reconstruction Using the DIEP and Pedicled TRAM Flaps: A 12-Year Clinical Retrospective Study and Literature Review
Bibliographic record
Abstract
BACKGROUND: There are few studies that compare the deep inferior epigastric artery perforator (DIEP) flap to the pedicled transverse rectus abdominis myocutaneous (pTRAM) flap for use in reconstructive breast surgery. The authors examined four factors that aid in decision-making: donor-site morbidity, need for surgery related to abdominal morbidity, operative time, and complications. METHODS: This is a retrospective review of patients undergoing breast reconstruction using the DIEP or pTRAM flap at the University of British Columbia between 2002 and 2013. The authors compared operative time and abdomen- and flap-related complications in both groups. RESULTS: Reconstruction was performed in 507 patients; 25.6 percent received DIEP flaps (n = 183 breasts) and 74.4 percent underwent pTRAM flap surgery (n = 444 breasts). Pedicled TRAM flap patients were more likely to require abdominal closure with mesh (44.2 percent versus 8.1 percent; p < 0.001); 21.2 percent of them had a postoperative bulge and/or hernia versus 3.1 percent of DIEP flap patients; and 12.7 percent of pTRAM flap patients required surgery for hernia/bulge. Controlling for confounders, there were five times the odds of a hernia/bulge in the pTRAM flap group. DIEP flap surgery was 234 minutes longer than pTRAM flap surgery. CONCLUSIONS: The benefits of the pTRAM flap may be offset by the need to correct abdominal wall complications. DIEP flap reconstruction had lower donor complications but increased operative time. A cost analysis is needed to determine the most economical procedure. CLINCIAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".