A Safe and Efficient Technique for Pedicled TRAM Flap Breast Reconstruction
Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Understand the indications for a unilateral pedicled transverse rectus abdominis (TRAM) flap-based breast reconstruction. 2. Understand the different types and designs of pedicled TRAM flap used in both immediate and delayed breast reconstruction. 3. Understand the essential landmarks and relevant anatomy of the pedicled TRAM flap. 4. Understand the steps of raising the pedicled TRAM flap, the subcutaneous transfer, and the insetting of the flap on the chest wall. 5. Understand the nature of donor-site management and closure of the defect. 6. Develop a postoperative plan for continuing care and pain management. SUMMARY: This article focuses primarily on the unilateral, ipsilateral pedicled TRAM flap. Although the bilateral pedicled TRAM flap may be a reasonable option in some cases, they have been shown to have a significant impact on abdominal wall strength and integrity. Other types of autogenous flaps using the same lower abdominal tissue, such as a free muscle-sparing TRAM or a deep inferior epigastric flap, can be performed as a bilateral procedure with less impact on the abdominal wall. Breast reconstruction with a pedicled transverse rectus abdominis flap has persisted for decades as a reliable and safe form of autologous breast reconstruction leading to a natural and stable breast shape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".