A Comparison of Commercially Available Polymethylmethacrylate-Based Soft Tissue Fillers
Bibliographic record
Abstract
BACKGROUND The rapid market expansion of filler treatment options requires physicians and health care providers to fully understand differences among comparable products. OBJECTIVE The objective was to compare commercially available polymethylmethacrylate (PMMA)-based soft tissue fillers to determine if there are meaningful variations in these products that could result in significantly different therapeutic profiles, especially with respect to safety. METHODS AND MATERIALS PMMA particles were evaluated for size and morphology using scanning electron microscopy (SEM) techniques. PMMA microsphere soft tissue filler products from the United States, Europe, Brazil, and Canada were compared with respect to size, homogeneity/irregularity, surface smoothness/roughness, and the presence or absence of sediment and particulate debris. RESULTS Marked differences with respect to PMMA particle morphology and related particle characteristics from a variety of products were found. Of note, some products demonstrated potentially concerning significant variability in particle size and irregular morphology. CONCLUSION It is anticipated that the variability detected in these products, based on the literature, could result in different therapeutic profiles, especially with respect to safety. Physicians and health care providers should be aware that "comparable" products that at a glance appear similar may not be equal.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".