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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".