Synthetic Dermal Fillers in Treating Acne Scars: A Comparative Systematic Review
Bibliographic record
Abstract
BACKGROUND: Acne is a common condition observed in adolescents and in most severe acne the scars develop. There are numerous treatment options for acne scars. However, no standardized guidelines have been established to guide physicians in the optimal treatment of acne scars. AIMS: The objective of this systematic review is to evaluate the existing evidence on various fillers used for the treatment of acne scars and to compare their effectiveness with one another. METHODS: The study was designed following PRISMA guidelines, and the information was retrieved in May 2024 using the PubMed database and ClinicalTrials.gov registry. The inclusion criteria were that studies involving patients of any age or gender with acne scars of any type treated with synthetic dermal fillers, and studies published in English. The exclusion criteria were studies with less than 10 participants and studies that did not use synthetic dermal fillers. To assess the risk of bias in the included studies, the Cochrane Collaboration's Risk of Bias tool was used for randomized controlled trials, and in observational studies, the Newcastle-Ottawa Scale was used. RESULTS: Twenty-six studies were included with a total of 1121 participants. Fourteen studies evaluated HA on 372 subjects, five studies focused on PMMA on 305 subjects, four on CaHA on 392 subjects, two on PLLA on 42 subjects, and one on PCL on 10 subjects. CONCLUSIONS: Most of the studies included in this review were of low quality, as indicated by their scores on quality assessments, lack of high-quality RCTs, and small sample sizes. Future research should focus on conducting randomized, controlled, split-face studies with an adequate number of participants and a detailed examination of different scar subtypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".