Comparing the Efficacy and Safety of Intralesional Verapamil With Intralesional Triamcinolone Acetonide in Treatment of Hypertrophic Scars and Keloids: A Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Clinical treatment of hypertrophic scars (HSs) and keloids is often unsatisfactory. Intralesional injections of triamcinolone acetonide (TAC) and verapamil are widely used to treat HSs and keloids, but their efficacy and safety are controversial. OBJECTIVES: The aim of this study was to conduct a meta-analysis of the effectiveness and safety of verapamil and TAC in the treatment of HSs and keloids. METHODS: Embase, Google Scholar, and PubMed were searched for randomized controlled trials (RCTs) from inception to February 2020. RCTs that evaluated treatment effects with the Vancouver Scar Scale or reported adverse effects were included. The continuous data and the dichotomous variables were analyzed as mean difference (MD) and relative risk (RR), respectively. RESULTS: Seven RCTs (461 patients) were included. Compared with verapamil, TAC rapidly changed the ∆height (MD = 0.07; P < 0.05) and ∆pliability (MD = 0.23; P < 0.05) after the first session, but subsequent treatments resulted in no significant differences in the ∆height, ∆pigmentation, ∆vascularity, and ∆pliability. Although total adverse effects (RR = 0.42; P = 0.1) were not significantly different, in the subgroup analysis the incidence of telangiectasia (RR = 0.04; P < 0.05) and skin atrophy (RR = 0.10; P < 0.05), but not pain (RR = 1.27; P = 0.77), was significantly lower with verapamil than with TAC. CONCLUSIONS: Verapamil may be an effective substitute for TAC. Although total adverse effects did not change, the incidence of telangiectasia and skin atrophy was lower with verapamil than with TAC.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".