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 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 | 0.002 |
| 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".