Therapeutic Efficacy of Intralesional Steroid With Carbon Dioxide Laser Versus With Cryotherapy in Treatment of Keloids: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Keloids are difficult to treat due to their poor response and high recurrence rate. OBJECTIVE: We conducted a randomized controlled trial to compare the therapeutic efficacy of intralesional triamcinolone acetonide (ILTA) in combination with carbon dioxide laser (Group 1) versus in combination with cryotherapy (Group 2) in the treatment of keloids. MATERIALS AND METHODS: Sixty patients with 101 keloids were randomized into 2 groups. On Day 1, keloids were ablated using either CO2 laser or cryotherapy followed by injection of ILTA at baseline and at 4 weeks interval for 3 months. Patients were followed up for 12 months to assess for therapeutic response and side effects. RESULTS: Successful therapeutic response (>50% improvement) between the 2 groups (CO2 vs cryotherapy) were assessed in terms of reduction in thickness, reduction in volume, patient's self-assessment, observer's assessment, and Vancouver Scar Scale score at the end of 6 months and 12 months (55.55% vs 70.37%; 61.1% vs 77.8%; 75% vs 77.78%; 61.12% vs 85.18%; 52.78% vs 62.96% respectively). The difference in therapeutic response between the 2 groups was not statistically significant at the end of 12 months. CONCLUSION: Both CO2 laser and cryotherapy in combination with ILTA were found to be equally effective in the treatment of keloids.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".