Survival properties of third-generation silicone gel breast implants
Bibliographic record
Abstract
From 1992 through 2001, 100 third-generation silicone gel breast implants were removed from 50 women who had undergone cosmetic breast augmentation. The main reasons for explantation were: ptosis in 16 patients (32%); to further increase implant size in 15 patients (30%); suspected silicone-related health problems in 11 patients (22%); medical disease in five patients (10%); and breast firmness and pain in three patients (6%). Of the 100 third-generation gel implants, 42 were manufactured by McGhan Medical, 38 by Surgitek, 10 by Cox-Uphoff and 10 by Dow Corning. The 42 McGhan implants had been in place for two to 15 years (mean 8.8 years), the 10 Cox-Uphoff implants for seven to 14 years (mean 9.4 years), and the 10 Dow Corning implants for five to 12 years (mean 8.1 years). All the McGhan, Cox-Uphoff and Dow Corning implants were clinically intact at explantation. By contrast, of the 38 Surgitek third-generation implants, which had been in place for three to 13 years (mean 7.9 years), only 28 were intact. Ten (26%) had already disrupted. A comparison of Kaplan-Meier survival curves indicated that the 62 third-generation gel implants manufactured by McGhan, Cox-Uphoff and Dow Corning were much more durable than 271 previously explanted second-generation gel implants. By contrast, the 38 third-generation Surgitek gel implants were less durable than the 271 second-generation implants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".