Prepectoral Passot-Type Immediate Breast Reconstruction With the Use of Acellular Dermal Matrix in Grades 2 and 3 Ptosis
Bibliographic record
Abstract
INTRODUCTION: Implant-based breast reconstruction after skin-sparing mastectomy remains one of the most frequently used methods of breast reconstruction in the US. Patients with large, ptotic breasts often face poorer outcomes. We hypothesized that implant-based breast reconstruction with auto-augmentation techniques can minimize problems with acellular dermal matrices (ADM) by using less, and providing the benefit of prepectoral placement. METHODS: We performed a single institution retrospective review of all patients with grade 2 or 3 ptosis following subpectoral and prepectoral BR with autologous dermal tissue and/or ADM over a 5-year period. Outcomes, complications, and costs between the subpectoral, prepectoral, and tissue expander (TE) with ADM groups were compared within our sample. RESULTS: Women in the Passot-type reconstruction groups had significantly higher body mass indices and rates of radiation therapy. When body mass index and radiation were controlled for, there were no significant differences in complication rates between prepectoral Passot, subpectoral Passot, and the TE/ADM group. Passot-type reconstructions can be significantly less expensive than TE/ADM. CONCLUSIONS: Prepectoral Passot-type reconstruction is a viable method to limit complications of subpectoral approaches at roughly half the cost of a TE/ADM approach.
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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 | 0.001 |
| 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 | 0.000 |
| 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".