Comparative study of the 80% trichloroacetic acid multiple puncture technique versus botulinum toxin type A in the treatment of keloid scars
Bibliographic record
Abstract
Background Keloid is one of the most challenging clinical problems encountered in wound healing. Although there are numerous treatment modalities, none of them have shown excellent therapeutic results. Aim The aim of the study was to evaluate the efficacies of 80% trichloroacetic acid (TCA) and botulinum toxin type A in the treatment of patients with keloid scars. Methods Thirty keloid patients were divided into two groups (each consisting of 15 patients). In group A, keloid scars were punctured using a punch instrument previously dipped in 80% TCA, whereas in group B keloid scars were injected intralesionally with botulinum toxin type A (2.5 U/cm3;). All patients underwent three to five therapeutic sessions 1 month apart, and follow-up for 1 year. The therapeutic response was determined according to the scores on the Vancouver scar scale and a self-assessment scale for pain and pruritus. Results In group A, the mean scores on the Vancouver scar scale before and after treatment were 9.73±1.33 and 4.94±2.44, respectively, with a total improvement of 49%. In group B, the mean scores on the Vancouver scar scale before and after treatment were 9.05±1.34 and 4.68±2.67, respectively, with a total improvement of 48%. Comparison between mean values obtained on the Vancouver scar scale in groups A and B after treatment showed a statistically nonsignificant difference. Group B showed better improvement as per the self-assessment score. Most common side effects were reported in group A and were in the form of hyperpigmentation (33.3%), hypopigmentation (6.7%), and mixed pigmentation (6.7%). Relapses occurred in 26.7% of patients in group A, whereas no relapses occurred in group B. Conclusions The 80% TCA multiple puncture technique is better than botulinum toxin type A in the treatment against 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".