Comparison between intralesional triamcinolone and intralesional methotrexate in the treatment of keloid
Bibliographic record
Abstract
Introduction/Aim. Keloid is a benign proliferative lesion of the dermal connective tissue. It is a challenging clinical problem, despite multiple therapies reported until now. The aim of the study was to determine the efficacy of intralesional methotrexate in the treatment of keloid in comparison to intralesional triamcinolone. Methods. This is an interventional comparative therapeutic study carried out at the Department of Dermatology in Al-Kindy Teaching Hospital, from April 2019 to January 2021. A total of 28 patients with 56 lesions were enrolled in this study; their ages ranged from 16 to 60 years, and they were satisfied with the selection criteria. Lesions were classified into two groups: Group A - 28 lesions treated with intralesional methotrexate and Group B - 28 lesions treated with intralesional triamcinolone. The treatment sessions were scheduled every four weeks. The Vancouver Scar Scale was used for the evaluation. A calculation of the mean decrease in total score was performed, and photographs were taken. Results. In both study groups, a significant reduction in height and pliability was seen in lesions treated with triamcinolone compared to lesions treated with methotrexate but no significant difference between the two drugs in vascularity and pigmentation were seen at the end of the study. Means of Vancouver Scar Scale in both groups after six months of treatment decreased significantly, and better results were seen with triamcinolone in comparison to methotrexate. Conclusion. The two modes of therapy were effective, however, better results were seen with triamcinolone in the treatment of keloid.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".