Strategies and Options for Free TRAM Flap Breast Reconstruction in Patients with Midline Abdominal Scars
Bibliographic record
Abstract
BACKGROUND: Patients who have a midline abdominal scar from previous abdominal surgery often present a challenge when use of a transverse rectus abdominis myocutaneous (TRAM) flap is desired for breast reconstruction. In this study, the authors reviewed their experience with the TRAM flap for breast reconstruction in patients with midline abdominal scars to evaluate the various strategies used to optimize reconstructive outcomes. METHODS: Between January of 1994 and December of 2001, 43 patients with a midline abdominal scar underwent unilateral autologous tissue breast reconstruction with a TRAM flap at The University of Texas, M. D. Anderson Cancer Center. RESULTS: The mean age of the midline abdominal scar was 13 years (range, 4 to 45 years). In 26 patients, only free hemi-TRAM flaps were used for breast reconstruction. Free TRAM flaps were used in nine patients in whom zone II across the midline scar had an adequate blood supply and was able to be incorporated into the flap for breast reconstruction. In five patients, blood supplies from both sides of the TRAM flap were used to augment perfusion to the tissue across the midline scar. In three patients with infraumbilical midline scars, a free TRAM flap was designed higher in the abdomen so that the superior half of the flap was scar-free. CONCLUSIONS: Various strategies are available for autologous tissue breast reconstruction using a free TRAM flap in patients with a previous midline abdominal surgical scar. In some cases, the TRAM flap tissue across the midline scar can be used reliably for breast reconstruction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".