Abstract QS2: Death By Implants: A Critical Analysis Of The FDA-MAUDE Database On Breast Implant-related Mortality
Bibliographic record
Abstract
Purpose: Since the 1996 moratorium by the Food and Drug Administration(FDA), the debate on the association of breast implants with systemic illnesses has been ongoing. Breast implant-associated anaplastic large cell lymphoma(BIA-ALCL) has also raised significant safety concerns in recent years. Methods: A systematic search of the U.S. Manufacturer and User Facility Device Experience (MAUDE) database was performed to identify and critically analyze all cases of breast-implant associated deaths reported to the FDA. Results: The search identified 50 reported cases of apparent implant-related mortality; BIA-ALCL comprised the majority of fatal outcomes (n=21, 42%), followed by lymphoma (n=4, 8%), breast cancer (n=3, 6%), pancreatic cancer (n=2, 4%), implant rupture (n=2, 4%), and postoperative infections (n=2, 4%). Single cases (n=1, 2% each) of leukemia, small bowel cancer, lung disease, pneumonia, autoimmune and joint disease, amyotrophic lateral sclerosis, liver failure, and sudden death, as well as 2 cases (4%) of newborn deaths, to mothers with breast implants, were also identified. A literature review demonstrated that 54% of alleged implant-related deaths were not truly associated with breast implant use: the majority of these reports (82%) originated from the public and third-party sources, rather than evidence-based reports by healthcare professionals and journal articles. Conclusions: While there exists a need for more comprehensive reporting in federal databases, the information available should be considered for a more complete understanding of implant-associated adverse outcomes. With only 46% of FDA- reported implant-related deaths demonstrated to be truly associated with breast implant use, there exists a need for public awareness and education on breast implant safety.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".