Breast implant illness: scientific evidence of its existence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: More than one million breast augmentation procedures using silicone breast implants (SBI) have been performed worldwide. Adverse events of SBI include local complications such as pain, swelling, redness, infections, capsular contracture, implant rupture, and gel-bleed. Furthermore, patients experience systemic symptoms such as chronic fatigue, arthralgias, myalgias, pyrexia, sicca, and cognitive dysfunction. These symptoms received different names such as autoimmune/autoinflammatory syndrome induced by adjuvants (ASIA) due to silicone incompatibility syndrome and breast implant illness (BII). Because of chronic immune activation, BII/ASIA, allergies, autoimmune diseases, immune deficiencies, and finally lymphomas may develop in SBI patients. AREAS COVERED: Causality for SBI-related BII/ASIA is reviewed. To address the role of silicone implants in promoting causality, we utilized the Bradford Hill criteria, with results highlighted in this article. EXPERT OPINION: We conclude that there is a causal association between SBIs and BII/ASIA. Using data derived from patients with BII/ASIA and from other medically implanted devices, there appears to be clear pathogenic relationship between SBI and BII/ASIA. Breast implants cause characteristic systemic reactions in certain women, leading to symptoms of sufficient severity to warrant device removal. The morbidity suffered is variable. SBI removal resolves the symptoms in most women, and removal is the most effective treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it