Comparing the Efficacy of Intralesional Verapamil Hydrochloride vs Triamcinolone Acetonide in Treatment of Keloids/ Hypertrophic Scars
Bibliographic record
Abstract
Objective: To compare the mean VSS score of intra lesional Verapamil Hydrochloride vs Triamcinolone Acetonide in treatment of keloids/ hypertrophic scars. Study type, settings & duration: This non randomized controlled trial was conducted in Sheikh Zayed Hospital, Rahim Yar Khan from July 2019 to June 2020. Methodology: In this study the cases of either gender and age more than 12 years were included irrespective of duration of keloid via non probability consecutive sampling. A total of 100 cases were included. The cases were divided into two equal groups by lottery method. The cases in group A were treated with intralesional Triamcinolone (40mg/ml) while group B was treated with intralesional Verapamil (2.5mg/ml).In both groups, session was repeated every 3 weeks for a maximum of 8 sessions or until complete flattening of the scar. Patients were accessed on the basis of Vancouver scar scale (VSS). VSS score was compared between both groups pre and post treatment. Results: The mean age in group A and B was 22.16±9.01 vs. 22.68±8.93 years respectively (p =0.49). The mean duration of keloid or scar was 6.58±1.54 months in group A and 6.46±1.42 in group B with p =0.31.VSS score after treatment was 2.41±1.19 vs 1.65±0.93 with respect to change in Pliability score and it was 1.61±0.81 vs 1.25±0.72 in terms of height in Verapamil vs Triamcinolone group with p values of 0.05 and 0.04 respectively. Conclusion: There is statistically significant difference in Triamcinolone group with respect to change in Pliability score and height as compared to......
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".