A cost‐effectiveness analysis of DIEP vs free MS‐TRAM flap for microsurgical breast reconstruction
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The deep inferior epigastric perforator (DIEP) flap may be associated with less long-term donor-site morbidity compared with free muscle-sparing transverse rectus abdominis myocutaneous flap (MS-TRAM) flap. However, DIEP flaps may have longer operative time and higher rates of acute postoperative complications. We performed a cost-effectiveness analysis (CEA) that compared the long-term costs and patient-reported outcomes between the two flaps. METHODS: A retrospective cohort of women who received free MS-TRAM or DIEP flap reconstruction between January 2008 and December 2012, with a minimum of 2-year follow-up, were recruited. Cost data of the primary reconstruction and any subsequent hospitalization due to complications from the reconstruction within 2 years were obtained. Each patient received a BREAST-Q questionnaire at 2 years post-reconstruction. RESULTS: In total, 227 patients (180 DIEP, 47 free MS-TRAM) were included. DIEP patients had significantly fewer abdominal hernia (P = 0.04). The adjusted-incremental cost-effectiveness ratios found that DIEP flap was more cost-effective to free MS-TRAM flap in the domains of "Physical Well-Being of the Abdomen" and "Satisfaction with Outcome." CONCLUSIONS: DIEP flap is the more cost-effective method of autologous breast reconstruction in the long-term compared with free MS-TRAM flap with respect to patient-reported abdominal well-being and overall satisfaction with the outcome.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".