Comparison of Fractional CO <sub>2</sub> Laser, Verapamil, and Triamcinolone for the Treatment of Keloid
Bibliographic record
Abstract
Objective: Scar biology is a territory less understood. The search for ideal treatment of keloid continues. The aim of this study was to compare the role of CO 2 laser, triamcinolone (TAC), and verapamil in the treatment of keloid. Approach: A randomized parallel-group study was conducted in which 60 patients were randomly allocated to three groups from May 2017 to April 2018. First group received fractional CO 2 laser therapy, second group received triamcinolone, and third group received intralesional verapamil. Outcomes were evaluated using Vancouver scar scale score at 3 weekly intervals for 6 months. Results: There was a reduction in scar height, vascularity, and pliability in all the three groups. However, pigmentation was not completely resolved by any of the three modalities. The response was fastest in case of triamcinolone followed by verapamil and laser, which was statistically significant. There was reduction in pain and pruritus in all the three groups and lesser injection site pain with verapamil. There was some amount of charring with CO 2 laser. Innovation: Our study provides evidence that TAC has the fastest response in treating keloids when compared to other modalities. Scar pigmentation is the parameter that is not completely resolved by TAC, verapamil, or CO 2 laser. Conclusion: The study revealed that fractional CO 2 laser and verapamil are as efficient as triamcinolone acetonide (TAC) for treating keloids, except it takes longer for laser and verapamil to act compared to TAC. Verapamil can be used as an alternative treatment modality that is cost-effective with minimal adverse effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".