Breast Implant–Related Adverse Events During Mammography
Bibliographic record
Abstract
BACKGROUND: Adverse events arising in patients with breast implants during mammography reported by the Food and Drug Administration include implant rupture, pain, and impaired visualization. However, data supporting these claims were collected in 2004, and since, newer implant generations have been developed with overall rate of implantation increasing by 48%. OBJECTIVES: This article aims to determine the current incidence of implant-related adverse events arising during mammography. METHODS: We analyzed reports regarding silicone and saline breast implants published in the Food and Drug Administration Manufacturer and User Facility Device Experience database between 2008 and November 2018. Search terms included "mammogram," "mammography," "radiograph," "breast cancer screening," "breast cancer test," and "x-ray." RESULTS: Of the 20 539 implant-related adverse events available in the Manufacturer and User Facility Device Experience database, 427 were retrieved using our search strategy and 41 were related to mammography. Thirty-five of identified cases (85.4%) reported implant rupture, of which 19 (54.3%) were confirmed by a healthcare professional, 9 (25.7%) were clinically confirmed by saline implant deflation, and 7 (20.0%) were unverified reports by patients. Sixteen ruptures (45.7%) occurred with silicone implants, whereas 19 ruptures (54.3%) occurred with saline. Other adverse events included pain (29.3%), change in implant appearance (14.6%), and swelling (7.3%). CONCLUSIONS: Although implant rupture, pain, change in implant appearance, and swelling may occur, minimal implant-related adverse events arise during mammography. Given the extremely low reported risk of implant rupture, this should neither prevent patients from adhering to breast cancer screening programs nor deter patients from seeking breast implants. Patients should be aware of these reported risks and discuss screening options with their breast cancer screening team.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".