Silicone Particles in Capsules Around Breast Implants: An Investigation Into Currently Available Implants in North America
Bibliographic record
Abstract
BACKGROUND: Breast implants have always been composed of a silicone elastomer envelope filled with either silicone gel or saline. Breast implant illness (BII) is a set of symptoms that has previously been linked to the leakage of silicone particles from the implants into the body. OBJECTIVES: Our research aimed to quantify the number of silicone particles present in the capsules of breast implants available in North America. METHODS: Thirty-five periprosthetic capsules were sampled and analyzed, and silicone particles were counted and measured. The capsule surface area was then measured and utilized to calculate particle density and total number of silicone particles. RESULTS: Eighty-five percent of capsules analyzed from silicone gel implants contained silicone, with an average of 62 particles per mm3 of capsular tissue. These implants had approximately 1 million silicone particles per capsule. In contrast, none of the saline implant capsules contained silicone. Capsules from macrotextured tissue expanders contained fewer and larger silicone particles. CONCLUSIONS: Silicone gel implants presented silicone particle bleeding into the periprosthetic capsule, totaling on average 1 million silicone particles per capsule. On the other hand, no silicone particle bleeding was observed from saline breast implants. These data suggest that particle bleeding comes from the inner silicone gel, and not from the smooth outer silicone shell. Previous studies have reported the presence of breast implant illness in patients with both silicone- and saline-filled implants. Therefore, our data suggest that silicone migration is not the sole cause of BII.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".