Complications and Burden of 2-Stage Tissue Expander to Implant-Based Reconstructive Surgery: A Single-Center Retrospective Study
Bibliographic record
Abstract
Introduction: Two-stage reconstruction with a tissue expander/implant (TE/I) technique remains the most common breast reconstructive approach following mastectomy. This study analyzes the post-operative complications and burden associated with 2-stage TE/I reconstruction independent of acellular dermal matrix (ADM). Methods: A retrospective chart review identified patients that underwent 2-stage, reconstruction with TE/I without ADM. Demographics, implant characteristics, tissue expansion information, and complications were recorded. Patients were followed for 3 months post-implant exchange. Logistic regression analysis was used to determine which variables were predictors for complications. Results: Ninety-one TE/I reconstructions without ADM were performed in 55 patients. The incidence of complications was 45% (n = 25). Mean complications per patient was 0.84 ± 1.2, with the most common being infection with the TE (n = 15, 24.2%). Mean number of fill appointments was 3.6 ± 1.7 (range: 1-8). Univariate linear regression showed for every increase in BMI, there was a 14.8 cc increase in implant volume, on average ( P < .001). Multivariable logistic regression model identified radiation history ( P = .036) and increasing BMI ( P = .049) as significant predictors for complications. Conclusion: Infection remains to be the leading cause of short-term complications in TE/I breast reconstruction patients. BMI and radiation are significant predictors. Larger, multicenter observational study data may elicit nuanced variation among different population demographics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".