The Efficacy of Combining Fractional Carbon Dioxide Laser With Verapamil Hydrochloride or 5-Fluorouracil in the Treatment of Hypertrophic Scars and Keloids: A Clinical and Immunohistochemical Study
Bibliographic record
Abstract
BACKGROUND: Ablative fractional laser-assisted therapy is increasingly used to facilitate drug delivery and intensify clinical efficacy of topically applied drugs. OBJECTIVE: To evaluate the effectiveness of combined ablative fractional CO2 laser and topically applied 5-fluorouracil (5-FU) or verapamil hydrochloride in the treatment of hypertrophic scars (HTSs) and keloids and to examine their possible effects on TGF-β1 expression. PATIENTS AND METHODS: Thirty patients with HTSs and keloids were randomly treated with combined CO2 laser followed by topical verapamil or 5-FU application or CO2 laser monotherapy. All patients received 4 treatments at 1-month intervals. Subjective and objective assessment was obtained using the Vancouver Scar Scale (VSS). Histological changes and immunohistochemical staining for TGF-β1 were performed. RESULTS: Compared with baseline, there was a significant reduction in the VSS 1 month after the last treatment session in all groups (p < .05). Laser-assisted 5-FU delivery tended to show a higher extent of improvement in scar characteristics than laser-assisted verapamil hydrochloride delivery, without significance. No significant side effects were reported in all patient groups. TGF-β1 expression was significantly decreased after laser sessions. CONCLUSION: Combined fractional CO2 laser and topical 5-FU or verapamil hydrochloride offer a safe therapy for HTSs and keloids.
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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) | 0.001 | 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".