Defining Breast Implant Illness: A Systematic Review and Meta-analysis of Patient-reported Symptoms
Bibliographic record
Abstract
Background: Although no definitive scientific link has been established, public concern surrounding breast implant illness (BII) is increasing. To study this potential condition, a clear definition is necessary. This systematic review aimed to characterize BII through a meta-analysis of patient-reported symptoms. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive literature search was conducted. The relative risk (RR) of the top 10 symptoms was meta-analyzed, comparing patients with and without breast implants. Additional analyses assessed whether surgical or patient-related factors influenced symptom occurrence. Results: A total of 36 articles were included in this study, accounting for 10,519 patients. Fatigue or malaise (RR = 3.15 [2.89–3.43]), myalgia or weakness (RR = 2.96 [2.76–3.18]), and cognitive dysfunction (RR = 2.87 [2.64–3.12]) were most strongly associated with the presence of breast implants. Implants that were ruptured (RR = 1.12 [1.04–1.21], P = 0.003) or filled with silicone (RR = 2.11 [1.49–2.99], P < 0.0001) appeared more likely to lead to BII-type symptoms. In contrast, patients who underwent explantation (RR = 0.94 [0.90–0.98], P = 0.003) or had implants for aesthetic reasons (RR = 0.91 [0.84–0.99], P = 0.02) reported fewer symptoms. Conclusions: Given increasing awareness and concern surrounding BII, it is essential for the plastic surgery community to critically examine patient outcomes. Establishing a consistent, symptom-based definition of BII and identifying key risk factors are necessary to guide future research and improve patient care.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".