Comparison of Breast Implant Deflation for Mentor Anterior and Posterior Valve Designs in Aesthetic and Reconstructive Patients
Bibliographic record
Abstract
BACKGROUND: Saline breast implant rupture remains problematic after implantation. Company reports and previous studies implicate the valve as a common site of implant failure. This study evaluates the rupture rate of the Mentor posterior valve compared with the anterior valve in breast augmentation and reconstruction. METHODS: This is a retrospective analysis of consecutive breast implantations performed between 1992 and 2004 by two surgeons. All but two implants were filled at or above the manufacturer-recommended volume. Data were collected by chart review, telephone survey, and Mentor Corp. reports. Kaplan-Meier and Mantel-Haenszel analyses were used to compare rupture rate and relative risks, respectively. RESULTS: Sufficient data were available for 516 implants in 325 women (average follow-up, 6.04 years). Overall, those implants with posterior valves had a lower rupture rate (0.007 versus 0.022). In the reconstructive cohort, the posterior valve implants had a lower rupture rate (0.011 versus 0.036), and the relative risk of rupture using an anterior valve versus a posterior valve was 3.387 (p = 0.0154). There was no significant difference in rupture rate between valve types in breast augmentation. A multivariate analysis showed that implant texture did not affect rupture rate. CONCLUSIONS: The authors found a statistically significant decrease in implant rupture for Mentor posterior valve implants in the reconstructive cohort and no difference in the augmentation cohort. Thus, the authors conclude that at worst, the posterior valve is not more prone to rupture than the anterior valve model. Furthermore, the authors believe that the postoperative flexibility of the posterior valve implants makes them more useful clinically.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".