Does Implant Surface Texture Affect the Risk of Capsular Contracture in Subglandular Breast Augmentation and Breast Augmentation-Mastopexy?
Bibliographic record
Abstract
BACKGROUND: Previous studies have reported decreased rates of capsular contracture associated with the use of textured surface breast implants placed in the subglandular plane during breast augmentation. However, since the publication of these studies, our understanding of the pathophysiology of capsular contracture, as well as the surgical techniques utilized to minimize bacterial contamination of the implant, have advanced considerably. OBJECTIVES: The purpose of this study was to re-evaluate the relation between implant surface texturization and capsular contracture rates for breast implants placed in the subglandular plane during primary breast augmentation. METHODS: Retrospective chart review was performed of all primary subglandular breast augmentation procedures involving the use of either smooth or textured round silicone gel implants, with or without simultaneous mastopexy. The primary outcome measures included clinically significant capsular contracture (Baker grade III/IV) and revision surgery for capsular contracture. RESULTS: Between 2010 and 2017, 526 patients underwent primary subglandular breast augmentation with either smooth (n = 212) or textured (n = 314) round silicone gel implants; 248 patients underwent breast augmentation, whereas 278 underwent breast augmentation-mastopexy. Average follow-up was 756 days in the textured group and 461 days in the smooth group. Five cases of capsular contracture were observed in the textured group, and 7 cases of capsular contracture were observed in the smooth group (P = 0.20). CONCLUSIONS: Smooth surface implants placed in the subglandular plane were not at a significantly increased risk of capsular contracture compared with textured surface implants. We suggest that adherence to a surgical technique focused on minimizing bacterial contamination of the implant is of greater clinical significance than implant surface characteristics when discussing 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 | 0.001 | 0.005 |
| 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 | 0.000 |
| 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".