Comparing the Efficacy of Intralesional Triamcinolone Acetonide With Verapamil in Treatment of Keloids: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Keloid management remains a challenging clinical problem despite numerous therapies reported until now. The efficacy of corticosteroids in the treatment of keloids has been well established. The most commonly used corticosteroid is intralesional triamcinolone. Sporadic reports on the use of intralesional verapamil suggest its efficacy. AIM: Since there is not sufficient evidence to support the role of intralesional verapamil as an effective alternative modality, it was decided to undertake a randomized study to determine its efficacy as a treatment for keloids. METHODS: A randomized, single-blind, single-group comparison with 15 patients (30 scars) was carried out to compare the effects of intralesional triamcinolone with verapamil injections. Injections were scheduled every 3 weeks accompanied by cryotherapy until complete flattening of the scar or maximum 8 sessions, whichever came earlier. Scar evaluation at each stage was done by serial photographic records as well as by Vancouver scar scale. Statistical analysis was done by Wilcoxon and Mann-Whitney U tests using SPSS version 19. RESULTS: In both study groups there was a reduction in height and pliability at the end of the study. Better improvement in height and pliability was seen with triamcinolone in comparison with verapamil. However, a desired change in vascularity and pigmentation was not seen with either of the drugs. CONCLUSION: Verapamil is not as effective as triamcinolone in the treatment of 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".