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 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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".