Current Status of Breast Implant Survival Properties and the Management of the Woman with Silicone Gel Breast Implants
Bibliographic record
Abstract
The survival properties of silicone gel breast implants are dependent on their vintage (year of manufacture), duration in situ and manufacturer. A total of 527 gel implants have been explanted and analyzed in the author's laboratories. Of the 28 first-generation implants (1963 to 1972), 27 (96.4%) remained intact after 14 to 28 years in situ (mean 20.8 years). Of the 216 second-generation implants (1973 to mid-1980s) that were explanted from 1992 to 1998, 158 (73%) had disrupted. Kaplan-Meier survival curves demonstrated significantly different survival properties among second-generation manufacturers. Surgitek implants were by far the least durable. After 14 years, all second-generation Surgitek implants had disrupted. By contrast, after 20 years, about half of the Dow Corning and Heyer-Schulte implants remained intact. Among third-generation implants (mid-1980s to 1992), 43 of 46 (93.4%) remained intact after a mean of 6.3 years (range three to 12 years). The three disruptions were Surgitek implants. Implants from other manufacturers remained intact. However, the disruption frequencies of third-generation implants have yet to be measured over the relevant periods of time. Survival patterns appeared to be related to the thickness of the elastomeric shell of the three generations of implants. Mechanical strength analyses of the elastomeric shells of explants have exhibited little or no large scale material degradation, even after as long as 28 years in situ. The mechanism of implant disruption likely involves the ‘fold flaw’ theory, whereby an internal abrasion can develop over time at the site of a fold in the implant wall. Diagnosis of disruption is difficult. Mammography is helpful only if there has been extravasation of silicone gel into breast tissue. Extravasation was observed in only 4.2% of second-generation implants removed from 1992 to 1998. It was not seen with first- or third-generation implants. Ultrasound analyses are not generally helpful to predict disruption because they are very operator dependent and because capsular contracture causes folds in the implant wall, which result in false positives. Magnetic resonance imaging is the most accurate imaging modality to detect implant disruption. However, this technology is not indicated for monitoring implant status because it is too costly and time consuming, and because it has significant limitations, particularly with first-generation and textured implants. Careful explantation and direct visual examination are the standards for diagnosing gel implant disruption. Many implant disruptions are likely ‘silent’, with no specific symptoms or clinical findings. After disruption, none of the following are elevated above the levels seen in control women without implant exposure: serum autoantibodies, blood and serum silicon, and the incidence of breast cancer, autoimmune disease or any other medical disease. There is no evidence to support the existence of any ‘novel’ or ‘atypical’ syndrome associated with gel implants. Women over 30 years of age with breast implants require regular monitoring for breast cancer detection. This should include monthly breast self examination and annual clinical breast examination. In addition, women over 50 years old require annual eight-view mammographic assessment using the implant displacement technique. Even then, breast implants have been shown to interfere with complete imaging, particularly if the implants are subglandular, large or associated with significant capsular contracture. A logical approach to explantation should involve consideration of a patient's personal concerns and anxiety, her implant vintage, the plane of insertion of her implants, her current clinical status and whether she chooses to replace her gel implants. Women requesting explantation require extensive information before deciding on surgery. At explantation, capsulectomy seems to be indicated if there is capsular calcification or major capsular thickening. (Pour le résumé, voir page suivante)
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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 | 0.001 |
| 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".