Acellular Dermal Matrix–sparing Direct-to-implant Prepectoral Breast Reconstruction
Bibliographic record
Abstract
INTRODUCTION: Refined mastectomy techniques, the advent of new technologies and materials such as acellular dermal matrix (ADM), cohesive gel silicone implants, and intraoperative tissue perfusion analysis, have fueled a resurgence in prepectoral breast reconstruction. This article aims to compare an immediate direct-to-implant prepectoral ADM-sparing approach with the traditional subpectoral 2-stage immediate reconstruction. A cost analysis within a Canadian-run single-payer system is also presented. METHODS: A retrospective 2-group comparative chart review study was performed (June 2015-January 2017) to identify all patients who underwent prepectoral direct-to-implant breast reconstruction using an ADM-sparing technique. The comparison group consisted of patients having undergone traditional 2-stage subpectoral reconstruction with ADM. All countable variables were included in the cost analysis, which was performed in Canadian dollars. RESULTS: A total of 77 patients (116 reconstructed breasts) were included. Both the prepectoral and subpectoral groups were comparable in size, demographics including age, diabetic and smoking status, and receiving neoadjuvant chemotherapy and postmastectomy radiotherapy. Patients having undergone direct-to-implant prepectoral reconstruction benefited from fewer follow-up visits (3.8 vs 5.4, respectively) and from less complications (24.7% vs 35.6%, respectively) including animation deformity. In addition, direct-to-implant prepectoral reconstruction costs 25% less than the 2-stage subpectoral reconstruction when all associated costs were considered. CONCLUSION: Prepectoral implant placement avoids many of the disadvantages of the traditional 2 stage subpectoral reconstruction, including pectoralis muscle dissection, animation deformity, and multiple surgeries. As the first comparative cost analysis study on the subject, our ADM-sparing direct-to-implant prepectoral reconstruction method costs 25% less than the traditional 2-stage subpectoral reconstruction with a comparable complication profile.
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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".