Evaluation of botulinum toxin type A for treating post burn hypertrophic scars and keloid in children: An intra‐patient randomized controlled study
Bibliographic record
Abstract
BACKGROUND: Consequently, the management of post burn hypertrophic scars and keloid in children are a great challenge for the physicians, parents, and children themselves. PURPOSE OF THE STUDY: To assess the efficacy and safety of treating hypertrophic and keloid scars with botulinum toxins injections. PATIENTS AND METHODS: This is a randomized intra-patient comparative study was conducted on 15 children with post burn hypertrophic and keloid scars. Children were randomized to receive Intralesional injection of botulinum toxins on one part of the hypertrophic scar/keloid where the other part was left as a control. The assessment of clinical improvement was measured by the Vancouver scar scale (VSS) and by skin analysis camera system. Sessions were performed every month for 6 months. RESULTS: Clinical and statistical dramatic improvement in the vascularity, pliability, and height of the lesions which have been injected with neuronox. Evaluation of the lesions by the Antera camera has proven marked changes in the vascularity and height. There was no correlations between Vancouver score improvement and variables such as the age, sex, skin type, and duration and lesion type. CONCLUSIONS: The botulinum toxins proved its efficacy and safety in treatment of hypertrophic scars and keloid in children. It improved the associated itching and pain. Moreover it improves the pliability, erythema, and thickness of the scars.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".