Patient Outcomes after Fat Grafting to the Radiated Chest Wall before Delayed Two-stage Alloplastic Breast Reconstruction
Bibliographic record
Abstract
Two-stage alloplastic breast reconstruction in patients having received mastectomy and radiation is associated with a high rate of complications. Fat grafting has been shown to mitigate the effects of radiation on the chest wall to allow for alloplastic reconstruction. In this study, we assess the outcomes (after a mean follow-up of 28 months), including complications and revisional procedures, of women who had fat grafting to the radiated chest wall before two-stage implant-based breast reconstruction. Methods: A retrospective chart review was performed on consecutive patients seeking delayed implant-based reconstruction after simple mastectomy and postmastectomy radiation therapy between 2011 and 2015. All patients underwent two sessions of fat grafting to the radiated chest wall before inserting a tissue expander and subsequent exchange to a silicone implant. Results: Twenty patients were included in the study. No reconstructive failures were recorded. The short-term complication rate was 5%, with one hematoma leading to a revisional procedure. The mean follow-up after reconstruction was 28 months. During follow-up, two patients (10%) developed capsular contracture grade IV with implant malposition, leading to capsular revision and implant exchange. Four patients (20%) underwent additional fat grafting for contour deformities. Conclusions: Fat grafting before two-stage alloplastic breast reconstruction in patients treated with mastectomy and postmastectomy radiation therapy may provide an alternate method of alloplastic reconstruction in a select group of patients who are not suitable for autogenous reconstruction. Follow-up data show that additional surgery may be required for correction of implant malposition and capsular contracture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".