Use of industrial liquid silicone: a scoping review
Bibliographic record
Abstract
PURPOSE: Illicit cosmetic injections remain highly prevalent and can cause serious complications, including death. We aimed to explore existing literature regarding the use of illicit cosmetic injections globally. METHODS: We searched six databases with no language restriction from inception to 2022. We included all articles focused on adult patients of any gender who received any illicit cosmetic injection. Screening and data extraction followed standards from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. RESULTS: After screening 629 abstracts and 193 full texts, 142 citations were included. We identified articles from 28 countries and three multi-country studies. Most were from high-income (75.3%) and upper-middle-income countries (21.8%). Of all patients whose gender identity was described, 49.9% were transgender women, and 40.8% were cisgender women. The anatomic regions most frequently injected were the buttocks (35%) and the breast (13.3%). The most frequently described complications were granuloma (41.5%), dermatological problems (41.5%), infection (35.9%), and pulmonary complications (34.5%). CONCLUSIONS: We observed the impact of illicit silicone injections, particularly on cisgender women and transgender individuals. Existing barriers must be addressed, including healthcare prejudice and inadequate knowledge about care for gender minorities. This will require educating at-risk groups and enhancing policies to regulate these procedures.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".