Silicone particles in breast implant capsules: A retrospective single-center study evaluating silicone bleeding
Bibliographic record
Abstract
Breast implants consist of a silicone elastomer shell filled with either silicone gel or saline solution. As per the Food and Drug Administration (FDA), breast implants offer safety and efficacy but pose short and long-term risks, including capsular contractures, silicone leakage, and discoveries of Breast Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL) and Breast Implant Illness (BII). This study aims to estimate the amount of silicone particles that leak from breast implants and deposit in the peri-prosthetic capsules of implants used in Belgium and investigate the various factors that may affect the magnitude of silicone leakage. From a monocentric registry from the plastic and reconstructive surgery department of University Hospital Brugmann, thirty-nine capsules’ slides were analyzed retrospectively using NDPView2 software. Silicone particles were counted and measured based on their largest diameter. The capsule surface area and its thickness were measured to calculate particle's density and total number of particles in each capsule. Silicone particles were detected in 32 (82%) out of 39 capsules, with significant higher densities observed in silicone implants compared to saline implants ( P = 0.027). Overall average density was 66 particle/mm 3 . The density of silicone particles increased significantly with prolonged implantation duration ( P < 0.001). Particle's density tended to differ between different implants’ textures and brands. Silicone particles were found in capsules of both saline and silicone implants. Silicone generates primarily from the silicone gel. Implants texture, brand, and duration of implantation are factors that influence silicone bleeding. Addressing silicone leakage is crucial for enhancing implant safety and patient well-being. Les implants mammaires sont constitués d’une enveloppe en élastomère de silicone remplie de gel de silicone ou de solution saline. Selon la Food and Drug Administration (FDA), les implants mammaires offrent sécurité et efficacité, mais présentent des risques à court et à long terme, notamment des contractures capsulaires, des fuites de silicone et des découvertes de lymphome anaplasique à grandes cellules associé aux implants mammaires (BIA-ALCL) et de maladie des implants mammaires (BII). Cette étude vise à estimer la quantité de particules de silicone qui s’échappent des implants mammaires et se déposent dans les capsules périprothétiques des implants utilisés en Belgique et à étudier les différents facteurs qui peuvent affecter l’ampleur des fuites de silicone. À partir d’un registre monocentrique du service de chirurgie plastique et reconstructrice de l’hôpital universitaire de Brugmann, trente-neuf lames de capsules ont été analysées rétrospectivement à l’aide du logiciel NDPView2. Les particules de silicone ont été comptées et mesurées en fonction de leur plus grand diamètre. La surface de la capsule et son épaisseur ont été mesurées pour calculer la densité des particules et le nombre total de particules dans chaque capsule. Des particules de silicone ont été détectées dans 32 (82 %) des 39 capsules, avec des densités significativement plus élevées observées dans les implants en silicone par rapport aux implants salins ( p = 0,027). La densité moyenne globale était de 66 particules/mm 3 . La densité des particules de silicone a considérablement augmenté avec la durée d’implantation prolongée ( p < 0,001). La densité des particules avait tendance à différer entre les différentes textures et marques d’implants. Des particules de silicone ont été trouvées dans des capsules d’implants salins et en silicone. Le silicone provient principalement du gel de silicone. La texture des implants, la marque et la durée d’implantation sont des facteurs qui influencent la perspiration prothétique du silicone. Le traitement des fuites de silicone est crucial pour améliorer la sécurité des implants et le bien-être des patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".