Facial Soft Tissue Augmentation With Bellafill: A Review of 4 Years of Clinical Experience in 212 Patients
Bibliographic record
Abstract
INTRODUCTION: Bellafill (Suneva Medical Inc) is a semipermanent injectable soft tissue filler composed of smooth and uniform polymethylmetacrylate (PMMA) microspheres suspended in a bovine collagen gel. It is a third generation PMMA filler, with more uniform shapes and sizes of the PMMA microspheres, which has been purported to decrease the incidence of granuloma formation. METHODS: We performed a retrospective review of our clinical experience from 2014 to 2017 with Bellafill as a soft tissue injectable filler in the following clinical scenarios: deep nasolabial folds, depressed facial acne scars, malar volume loss, temporal wasting, tear trough deformity, chin augmentation, angle of jaw augmentation, and lip augmentation. The primary outcome is the rate of adverse events, and the secondary outcome is subjective patient satisfaction. RESULTS: From 2014 to 2017, 842 syringes of Bellafill were administered to 212 patients, for a total of 417 procedures. Of the 417 procedures, 96 (23.0%) were for acne scars, 82 (19.7%) malar volume restorations, 65 (15.6%) nasolabial fold augmentations, 45 (10.8%) chin augmentations, 42 (10.1%) tear trough volume restorations, 28 (6.7%) temple volume restorations, 25 (6.0%) rhinoplasty touch-ups for small areas of nasal depression, 22 (5.3%) lip augmentations, and 12 (2.9%) jaw angle augmentations were performed. A range of 1 to 12 syringes were injected into each patient, over 1 to 3 sessions; 6 cases of adverse events occurred (1.4%). There were 4 cases of solitary nodules in the injection site, 1 case of lower eyelid oedema which persisted for 3 months and 1 case of lower lip oedema which resolved within hours. Patient satisfaction rates ranged from 83.3% for angle of jaw augmentation to 99.0% for improvement of acne scars. CONCLUSION: Bellafill is a safe and effective option for a semipermanent soft tissue filler, with high patient satisfaction and a good safety profile.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".