Conversion from Alloplastic to Autologous Breast Reconstruction: What Are the Inciting Factors?
Bibliographic record
Abstract
Introduction: Failure of alloplastic breast reconstruction is an uncommon occurrence that may result in abandonment of reconstructive efforts or salvage with conversion to autologous reconstruction. The purpose of this study was to identify factors that predict failure of alloplastic breast reconstruction and conversion to autologous reconstruction. Methods: A retrospective chart review was conducted of patients who underwent mastectomy and immediate alloplastic breast reconstruction between 2008 and 2019. Inclusion criteria included patients 18 years or older who underwent initial alloplastic reconstruction with a minimum of 3-year follow-up. Data collected included age, body mass index, cancer type, surgical characteristics, neo/adjuvant treatment details, and complications. Results were analyzed using Fischer's exact test, t-test, and multivariate logistic regression. Results: A total of 234 patients met inclusion criteria. Of those, 23 (9.8%) required conversion from alloplastic to autologous reconstruction. Converted patients had a mean age of 50.1 ± 8.5. The time from initial alloplastic reconstruction to conversion was 30.7 months. The most common reasons for conversion included soft tissue deficiency (48%), infection (30%), and capsular contracture (22%). Patients were converted to deep inferior epigastric perforator flap (DIEP; 52%), latissimus dorsi flap with implant (26%), and DIEP with implant (22%). Multivariate logistic regression modeling identified radiation (OR 8.4 [CI = 1.7-40.1]) and periprosthetic infection (OR 14.6 [CI = 3.4-63.8]) as predictors for conversion. Conclusions: Among patients undergoing mastectomy with immediate alloplastic breast reconstruction, those treated with radiation have 8.4 greater odds of conversion and those with a periprosthetic infection have 14.6 greater odds for conversion to an autologous reconstruction.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".