Comparing Two Methods of Cryotherapy and Intense Pulsed Light with Triamcinolone Injection in the Treatment of Keloid and Hypertrophic Scars: A Clinical Trial
Bibliographic record
Abstract
Keloid and hypertrophic scars are abnormal manifestations of wounds that occur following skin injuries in the form of local proliferation of fibroblasts and increased production of collagen. There are several ways to cure these scars; treatment must be selected based on the nature of the scars. In this clinical trial, two methods—cryotherapy and intense pulsed light (IPL)—are compared in the treatment of scars, and the results are presented in terms of improvement level, complications, and patient satisfaction. This clinical trial was conducted in southeastern Iran. The intervention group included scars that underwent the IPL method and the control group, which consisted of scars that were subjected to cryotherapy. In both methods, intralesional corticosteroid injection was administered. To select samples, the easy sampling method was used. To determine the expected outcomes, the criteria determined in the Vancouver scar scale were used. Data were analyzed using the Mix Model, chi-square test, and t test. In this study, 166 samples of keloid and hypertrophic scars were cured using two methods (Cryotherapy, 83; IPL, 83). The recovery rate was higher in the Cryotherapy group than in the IPL group (p > 0.05), and the incidence of complications was also higher in the Cryotherapy group (14.5% vs. 12%). Moreover, patients were more satisfied, although not significantly so, with the cryotherapy method (p = 0.09). Both methods were highly successful in curing scars; participants were totally satisfied with both methods.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".