Comparison of Ultrasound‐Assisted Low‐Dose Versus Medium‐Dose 5‐Fluorouracil and Triamcinolone Acetonide in the Treatment of Hypertrophic Scar
Bibliographic record
Abstract
Intralesional 5‐fluorouracil (5‐FU) and triamcinolone acetonide (TAC) injection is effective for the treatment of hypertrophic scar. The side effects of current that recommended 45 mg/ml (high‐dose) 5‐FU have been reported. However, no previous study has investigated the efficacy and safety of low‐dose (2.5 mg/ml) 5‐FU with 4 mg/ml TAC or medium‐dose (10 mg/ml) 5‐FU with 4 mg/ml TAC for treatment of hypertrophic scar. Herein, a retrospective comparative study was conducted. The records of 70 patients, treated with low‐dose (2.5 mg/ml) 5‐FU and 4 mg/ml TAC every 4 weeks (Group 1) or medium‐dose (10 mg/ml) 5‐FU and 4 mg/ml TAC every 4 weeks (Group 2), were analyzed. The Vancouver Scar Scale (VSS), vascularity, and thickness of hypertrophic scar at baseline and at 7th‐treatment (each group received 6 treatment sessions) were compared. The ultrasound showed the large vascular distribution in scar margins. Both groups gained clinical improvement in VSS, vascularity, and thickness. Group 2 (medium‐dose) exhibited significantly better improvement than Group 1 (low‐dose). However, the overall side effects rate was 11.4% in Group 1, significantly lower than 31.4% in Group 2. Scar margins were suggested to be target sites for injection. Medium‐dose (10 mg/ml) 5‐FU + 4 mg/ml TAC could effectively reduce the thickness of hypertrophic scar; however, the side effects rate was also higher in medium‐dose group than in low‐dose group.
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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".