Combined Fractional Erbuim-YAG Laser With Botulinum Toxin-A Versus Botulinum Toxin-A Alone For The Treatment Of Hypertrophic Scars And Keloids
Bibliographic record
Abstract
Background: The treatment of hypertrophic scars (HTS) and keloids remains a challenge. Not all treatment modalities have been adequately tested. Objectives: We aimed to compare the efficacy between combined fractional Er:YAG with intra-lesional botulinum toxin (Botox) and intra-lesional Botox as a monotherapy for the treatment of HTS and keloids. Patients and methods: Thirty patients with HTS and keloids were treated by intra-lesional injection of Botulinum Toxin Type A (Botox) as a monotherapy and Botox combined with ablative fractional Er:YAG laser. Each lesion was divided into two parts. The allocation of treatment method was randomly selected. One part was treated with Botox intra-lesionally 5 IU/cm2. The other part was subjected to combined intra-lesional Botox and ablative fractional Er:YAG laser (2,940 nm) sessions (4 sessions every 4 weeks). Evaluation of the treatment outcomes was done by the Vancouver Scar Scale (VSS), clinical imaging, and immuno-histochemical studies. Results: There was a significant decline in VSS after treatment with the combined regimen compared to the sites treated with botox injection only (P
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.002 | 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".