Single-Stage Breast Reconstruction Using an All-In-One Adjustable Expander/Implant
Bibliographic record
Abstract
Background: When tissue expansion is necessary in breast reconstruction, a single-stage approach is possible using adjustable expander/implants, with or without the use of acellular dermal matrix. We aimed to present the senior author’s single-stage experience over a period of 12 years using combined expander/implants in breast reconstruction. Methods: This is a Single-institution, retrospective review of breast reconstruction with combined expander/implants from 2002 to 2014. Logistic regression was performed to evaluate the impact of multiple variables on long-term outcomes. Results: A total of 162 implants in 105 patients were included in this study. Mean follow-up time was 81.7 months (SD, ± 39.2; range, 15–151). Complication rates were as follows: 0.62% extrusion, 1.2% mastectomy flap necrosis, 1.2% hematoma, 1.9% dehiscence, 2.5% seroma, 4.9% infection, and 15.4% deflation. The following associations were identified by logistic regression: adjuvant radiotherapy and capsular contracture (P = 0.034), tumor size and deflation (P = 0014), and smoking history and infection (P = 0.013). Conclusions: Overall, 81% of breasts were successfully reconstructed in a single stage. Single-stage reconstruction using all-in-one expander/implants reduces costs by eliminating the need for a second procedure under general anesthesia and can achieve results comparable with other alloplastic reconstructions reported in the literature.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".