Efficacy of Intralesion Injection of Combined 5-Flourouracil and Triamcinolone versus Triamcinalone alone in Keloids and Hypertrophic Scars: A Comparative Analysis
Bibliographic record
Abstract
Objective: The treatment of keloids and hypertrophic scars is challenging and controversial. The therapeutic agents found in the literature include silicone sheets, compression garments, corticosteroid injections, 5-fluorouracil (5-FU), bleomycin and interferon, topical imiquimod, cryotherapy, radiation, and laser or light-based therapies. Triamcinolone acetonide (TCA), a corticosteroid, considered first line treatment for the prevention and treatment of keloids and hypertrophic scars. To compare the efficacy of triamcinolone acetonide alone and triamcinolone acetonide plus 5-florouracil for treating keloids and hypertrophic scars in burn patients. Methodology: In this study, the patients were divided into two groups A and B on the basis of treatment regimen, i.e. Group A (TCA alone) and Group B (5FU+TCA). The efficacy of both treatments was compared for improvement in Vancouver scar scale (VSS) and pruritus scale. Results: The mean VSS score pretreatment was calculated as 10.74±2.36 in Group A and 10.27±3.14 in Group B. Post-treatment, it was reduced to 5.58±1.04 in Group-A and 3.41±2.11 in Group-B. The comparison of efficacy shows an improvement of 65.80% in Group A and 75.07% in Group B; the p value was 0.047, showing a significant difference. Conclusion: Combination therapy of intra-lesion injection of triamcinolone acetonide and 5-florouracil has significantly higher efficacy as compared to triamcinolone acetonide alone for the treatment of keloids and hypertrophic scars, but necessary precautions have to be taken.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".