Predictors of Mastectomy Flap Necrosis in Patients Undergoing Immediate Breast Reconstruction
Bibliographic record
Abstract
BACKGROUND: Mastectomy flap necrosis (MFN) after mastectomy and immediate breast reconstruction can compromise postsurgical recovery, lead to additional surgeries, and compromise aesthetic outcome. The objective of this study was to determine if there is a difference in the rate of MFN in patients undergoing immediate alloplastic versus immediate autologous breast reconstruction. The secondary objective was to identify additional patient and surgical factors that may influence the rate of MFN. METHODS: A retrospective chart review of patients who underwent immediate breast reconstruction between 2003 and 2011 in the University of British Columbia Breast Program was performed. Demographic, oncologic, reconstructive, and surgical data were compiled. RESULTS: Approximately 404 alloplastic and 314 autologous patients were reviewed. The overall rate of MFN was 12.9%. There was a trend toward a higher MFN rate in the autologous patient group (15.2% vs 11.6%, P = 0.095). After controlling for age, body mass index (BMI), smoking status, preoperative breast radiation, surgery duration, cancer side, mastectomy type, and postoperative chemotherapy, no association was found between reconstruction type and MFN. BMI greater than 30, smoking status, and preoperative radiation were independent predictors of MFN. Surgical factors including longer duration of surgery and Wise pattern mastectomy incision were also found to be associated with increased odds of MFN. CONCLUSION: We found no difference in the rate of MFN when comparing immediate alloplastic and autologous reconstruction methods. A number of patient and surgical factors were found to be predictors of MFN. The results of this large, retrospective study will help surgeons to tailor their reconstruction based on a patient's risk factors to minimize the incidence of MFN.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".