Quality of Life and Patient-Reported Outcomes in Breast Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Approximately 20 percent of women select autologous tissue for postmastectomy breast reconstruction, and most commonly choose the abdomen as the donor site. An increasing proportion of women are seeking muscle-sparing procedures, but the benefit remains controversial. It is therefore important to determine whether better outcomes are associated with these techniques, thereby justifying longer operative times and increased costs. METHODS: Patients from five North American centers were eligible if they underwent reconstruction by means of the deep inferior epigastric artery perforator (DIEP) flap, muscle-sparing free transverse abdominis myocutaneous (TRAM) flap, free TRAM flap, or the pedicled TRAM flap. Patients were sent the BREAST-Q. Demographics and complications were collected. RESULTS: The authors analyzed 1790 charts representing 670 DIEP, 293 muscle-sparing free TRAM, 683 pedicled TRAM, and 144 free TRAM patients with an average follow-up of 5.5 years. Flap loss did not differ by flap type. Partial flap loss was higher in pedicled TRAM compared with DIEP (p = 0.002). Fat necrosis was higher in pedicled TRAM compared with DIEP and muscle-sparing free TRAM (p < 0.001). Hernia/bulge was highest in pedicled TRAM (p < 0.001). Physical well-being (abdomen) scores were higher in DIEP compared with pedicled TRAM controlling for confounders. CONCLUSIONS: Complications and patient-reported outcomes differ when comparing abdominally based breast reconstruction techniques. The results of this study show that the DIEP flap was associated with the highest abdominal well-being and the lowest abdominal morbidity compared with the pedicled TRAM flap, but did not differ from muscle-sparing free TRAM and free TRAM flaps. CLINICAL 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".