Plastic Surgeons Defend Textured Breast Implants at 2019 U.S. Food and Drug Administration Hearing: Why It Is Time to Reconsider
Bibliographic record
Abstract
Textured breast implants were the subject of a U.S. Food and Drug Administration (FDA) hearing on March 25 and 26, 2019. Regulating agencies in other countries, including all of Europe and Canada, have already banned macrotextured implants. Patients affected by Breast Implant-Associated Anaplastic Large-Cell Lymphoma (BIA-ALCL) recounted their life-changing experiences, and requested a ban on textured devices. Plastic surgeons, many with industry ties, spoke in favor of keeping the devices available. The historical advantages of textured implants were presented, including a reduced capsular contracture rate. A 14-point plan to improve sterility at the time of implantation was promoted as an effective alternative to reduce both capsular contractures and BIA-ALCL risk. However, recent studies show that textured implants have not delivered on their early promise. Biocell implants perform worse, not better, than other implant types, and capsular contracture rates are not significantly reduced according to recent core studies. The only known risk factor for BIA-ALCL is textured implants. The lifetime risk for Biocell implants is at least 1:2, 200. There is no reliable evidence that surgical technique makes a difference in risk. This serious issue represents a case study of conflict of interest. In light of recent information, a re-analysis of the true risks and benefits of textured implants is justified. It is time for our professional societies to recognize that the device is the problem rather than surgical technique. On May 2, 2019, the FDA decided against a ban on textured breast 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".