Prospects for Use of Botulinum Toxin Type A for Prevention of Hypertrophic and Keloid Scars after Surgeries
Bibliographic record
Abstract
Abstract Objective To evaluate the possibility of improving and preventing the formation of postoperative hypertrophic and keloid scars using botulinum toxin type A (BTA). Materials and Methods Scientific articles published in English have been systematically screened in PubMed/MEDLINE database over the entire period. The following information about the studies was analyzed: first author surname; year of publication; number of patients; average age; scar location; dosage of the drug administered; follow-up duration; scar assessment methods; results, incidence of hypertrophic and keloid scars formation. The odds ratio and 95% confidence interval were calculated for each of the estimated parameters. The statistical heterogeneity of publications assessed using the criteria of chi-square test and I 2. The differences were considered significant at p < 0.05. Results A total of 18 prospective randomized studies were selected for evaluation, containing data on the use of BTA in 363 cases. Patients receiving botulinum toxin had a lower Vancouver scar scale index, higher visual analog scale index, and higher Stony Brook scar evaluation scale score. The use of BTA reduces the risk of perceptible scar formation, the incidence of hypertrophic and keloid scars. Conclusion The use of BTA to obtain imperceptible scar and prevent hypertrophic and keloid postoperative scars demonstrates good prospects. However, there is no consensus regarding the pathophysiological mechanisms underlying the positive effect of BTA on the prevention of hypertrophic and keloid 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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".