Comparisons of the effects of topical anti-scar drugs on post-surgical facial scar formation: a clinical investigation.
Bibliographic record
Abstract
OBJECTIVES: Scarring is a common but intricate problem, and topical anti-scarring drugs are the most widely used treatment. However, the wide range of drugs available makes it difficult for doctors and patients to choose from because of the lack of clinical comparisons. Therefore, we conducted an observational study to compare the clinical efficacy of different topical anti-scarring drugs. METHODS: Patients with post-suturing facial scars were enrolled in this study. The questionnaire was designed to record the basic characteristics of the patients. The Vancouver Scar Scale, SCAR scale, and measurements of scar width and thickness were used to evaluate scar quality. Patients who met the inclusion criteria were divided into four groups for comparison: the silicone preparation (SP), onion extract (OE), asiaticoside (AC) groups, and the untreated blank control (BC) group. The overall data were analyzed before they were confined to the zygomatic region. RESULTS: A total of 127 eligible patients were enrolled in this study. The results of the total and zygomatic scars demonstrated that SP, OE, and AC groups resulted in narrower scars and lower scar scale scores. The SP group depicted higher melanin efficacy than the other two groups. The OE group had the best pliability, whereas the AC group had the thinnest scar. CONCLUSIONS: In this study, we acquired expertise with different topical anti-scar agents: SP significantly reduced melanin levels, OE mainly benefited scar pliability, and AC was better at reducing scar thickness. These differences may be more instructive for clinical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".