Efficacy and safety of sequential treatment with botulinum toxin type A, fractional CO2 laser, and topical growth factor for hypertrophic scar management: a retrospective analysis
Bibliographic record
Abstract
Hypertrophic scars arise from aberrant wound healing and can lead to functional and aesthetic impairments. One of the common interventions for treating hypertrophic scars is fractional carbon dioxide (CO2) laser, which employs narrow laser beams to stimulate dermal collagen deposition. Recent studies and reports have suggested that combining laser therapy with other interventions such as botulinum toxin type A (BTX-A) and topical growth factors may enhance treatment outcomes. Here, we examine the efficacy and safety of a sequential combination of BTX-A, fractional CO2 laser, and topical growth factors, referred to as combined therapy, for treating hypertrophic scars compared with only using fractional CO2 laser and topical growth factors, referred to as monotherapy. Our retrospective study includes 128 patients with hypertrophic scars (56 underwent monotherapy and 72 underwent combined therapy), which were followed-up for up to 15 months after the initiation of treatment to collect demographic and clinical data. Our analysis showed that the combined therapy significantly outperformed monotherapy in improving Vancouver scar scale scores (P < 0.05) and in the reduction of scar thickness (P < 0.05), without increasing adverse complications. Repeated treatments further augmented the efficacy of the combined therapy. Subgroup analysis revealed that combined therapy was notably more effective in reducing Vancouver scar scale scores and scar thickness in early-stage scars compared to late-stage (P = 0.023 and P = 0.045, respectively). Our study suggests that including BTX-A treatment before fractional CO2 laser and topical growth factors offers superior efficacy in reducing hypertrophic scars. We encourage early intervention and repeated treatments for optimal treatment outcomes.
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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 | 0.001 | 0.001 |
| 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".