Direct-to-Implant, Prepectoral Breast Reconstruction: A Single-Surgeon Experience with 201 Consecutive Patients
Bibliographic record
Abstract
BACKGROUND: The resurgence of prepectoral breast reconstruction has brought strict patient inclusion and exclusion criteria by numerous authors. This article provides an overview of a single surgeon's experience with 201 patients, 313 breasts using immediate, direct-to-implant prepectoral breast reconstruction. The article compares surgical outcomes of different patient cohorts to elucidate risk factors that may predispose patients toward developing complications. METHODS: A retrospective chart review was performed, identifying all patients who underwent prepectoral, direct-to-implant breast reconstruction from June of 2016 to June of 2018. RESULTS: A total of 201 patients representing 313 breasts were included. A midlateral incision was used in 157 breasts (50.2 percent), followed by a skin-reducing, Wise-pattern in 90 breasts (28.8 percent). Acellular dermal matrix was used in 243 breasts (77.6 percent), free nipple grafts were used in 39 breasts (12.5 percent), and postmastectomy radiation therapy was used in 58 breasts (18.5 percent). Complications requiring operative intervention occurred in 24 breasts (7.7 percent), and minor complications occurred in 23 breasts (7.3 percent). There were no significant differences in complication rates for (1) acellular dermal matrix use versus non-acellular dermal matrix use, (2) Wise-pattern versus other incision, or (3) postmastectomy radiotherapy (p > 0.05). CONCLUSIONS: This represents the largest single-surgeon, direct-to-implant prepectoral cohort in the literature. Surgical complications did not differ with acellular dermal matrix use, incision selection, and the use of postmastectomy radiation therapy. There may be an association between acellular dermal matrix use and major complications and radiotherapy with minor complications. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".