Death by Implants: Critical Analysis of the FDA-MAUDE Database on Breast Implant-related Mortality
Bibliographic record
Abstract
Since the 1992 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 has also raised significant safety concerns in recent years. METHODS: A systematic search of the Manufacturer and User Facility Device Experience (MAUDE) database was performed to identify all cases of breast implant-associated deaths reported to the FDA. RESULTS: The search identified 50 reported cases of apparent implant-related mortality; breast implant-associated anaplastic large cell lymphoma 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, and 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 health-care professionals and journal articles. CONCLUSIONS: Although 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 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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".