Direct-to-Implant Single-Stage Immediate Breast Reconstruction with Acellular Dermal Matrix
Bibliographic record
Abstract
BACKGROUND: Direct-to-implant single-stage immediate breast reconstruction using acellular dermal matrix is a cost-effective alternative to two-stage expander-implant reconstruction. The purpose of this study was to identify predictors of direct-to-implant single-stage immediate breast reconstruction failure, defined as need for early (≤6 months) revision surgery. METHODS: The authors conducted a retrospective cohort study of all patients with direct-to-implant single-stage immediate breast reconstruction in 2010 and 2011 at three University of British Columbia hospitals. Data were compared between successful and failed single-stage reconstructions. Predictors of failure were identified using multivariate logistic regression. Patient demographics and complications were compared to a random sample of control patients with two-stage alloplastic reconstruction without acellular dermal matrix. RESULTS: Of 164 breasts that underwent direct-to-implant single-stage immediate breast reconstruction, 52 (31.7 percent) required early revision. Increasing breast cup size was the only significant predictor of early revision compared with bra size A (OR for bra size B, 4.86; C, 4.96; D, 6.01; p < 0.05). Prophylactic mastectomies showed a trend toward successful single stage (OR, 0.47; p = 0.061), whereas smoking history trended toward failure (OR, 1.79; p = 0.065). Mastectomy flap necrosis was significantly higher in direct-to-implant single-stage immediate reconstruction cases compared to two-stage controls. CONCLUSIONS: Direct-to-implant breast reconstruction can be reliably performed in a single stage in patients with small breast size. Increasing breast cup size confers a higher chance of early revision. A two-stage approach may be more cost-effective in larger breasted patients. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".