Textured Surface Breast Implants in the Prevention of Capsular Contracture among Breast Augmentation Patients: A Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Capsular contracture is a common complication associated with the use of breast implants. Numerous randomized controlled trials addressing the efficacy of textured surface breast implants in reducing capsular contracture have yielded nonuniform results. This meta-analysis addresses the use of textured breast implants in the prevention of capsular contracture. METHODS: MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials databases were searched to identify all randomized controlled trials involving the use of textured versus smooth breast implants. The results of these trials were meta-analyzed to obtain a pooled odds ratio of the effect of textured surfacing on capsular contracture rates. In addition, subgroup analyses were performed based on implant type (saline or silicone gel), type of surface texturing (Siltex or Biocell), placement (subglandular or submuscular), and length of follow-up. RESULTS: Eleven trials were reviewed. Four were excluded because they failed to meet a priori inclusion criteria. The remaining seven trials were meta-analyzed. Only three of these studies found significantly lower rates of capsular contracture with the use of textured implants. However, when all seven studies were pooled, the odds ratio was found to be 0.19 (95 percent confidence interval, 0.07 to 0.52), indicating a protective effect for surface texturing on the rate of capsular contracture. Submuscular placement was the only subgroup in which significance was not achieved. However, this subgroup consisted of a single study, which was dramatically underpowered. CONCLUSION: The results of this meta-analysis demonstrate the superiority of textured over smooth breast implants in decreasing the rate of 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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.047 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".