Comparison of Intralesional Triamcinolone and Intralesional Verapamil in The Treatment of Keloids
Bibliographic record
Abstract
An imbalance between synthesis and degradation of collagen and the extracellular matrix leads to keloid formation. Inadequately treated keloids lead to significant physical and emotional distress. Objectives: To compare the mean reduction of the Vancouver Scar score in patients with keloids after 3 months of treatment with intralesional triamcinolone versus intralesional verapamil. Methods: This parallel‐group, single‐blind randomized controlled trial was performed at the dermatology department, Nishtar Hospital, Multan, from April 2025 to September 2025. Sixty patients aged 10–50 years with keloids (1–5 cm, duration <5 years, baseline Vancouver score ≥5) were enrolled. Exclusion criteria were pregnant and lactating women, positive family history of keloids, conditions of acromegaly, and congestive heart illnesses. Group A (n=30) received intralesional verapamil monthly, and Group B (n=30) received intralesional triamcinolone acetonide (40 mg) monthly until keloid flattening or for three months. Vancouver scores were assessed at 16 weeks (four weeks post-treatment), and data were analyzed using SPSS version 23.0. Mean ± SD was recorded for quantitative and frequencies and percentages for categorical data. An independent sample t-test was used for numerical comparison at 5% significance level. Results: The mean age was 28.6±7.9 years with 58.3% males. The baseline Vancouver score was 8.7 ± 1.7, improving to 4.9 ± 1.5 after treatment. The triamcinolone group had a lower score (4.0 ± 0.7) and greater reduction (4.6±1.2) than the verapamil group (5.9 ± 1.5; reduction 2.9 ± 0.8; p<0.001). Conclusions: Overall, intralesional triamcinolone acetonide is clearly more effective than verapamil in reducing keloid severity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".