Fractional carbon dioxide (CO₂) laser alone versus fractional CO₂ laser combined with triamcinolone acetonide or tricholoroacetic acid in keloid treatment: A comparative clinical & radiological study
Bibliographic record
Abstract
Introduction: Keloids are benign fibro-proliferative scarring extending outside the initial wound. Different modalities of treatment as intralesional corticosteroid injection, fractional CO₂ laser and others can be used either as mono or combined therapies. Objectives: To assess the role of fractional CO2 laser versus fractional CO2 laser accompanied with either triamcinolone acetonide or trichloroacetic acid 20% in keloid treatment clinically and radiologically. Methods: The current study was conducted on 45 Egyptian participants with keloid scar at different sites of the body. They were classified into three groups treated by fractional CO2 laser only (group I), fractional CO2 laser followed by triamcinolone acetonide (group II) or trichloroacetic acid application (group III) respectively. Evaluation of the keloid was done with Vancouver Scar Scale (VSS) and Color Doppler Ultrasound (CDU) before and after treatment. Four sessions, one month apart were applied for the patients. They were followed up for 8 weeks after the last session. Results: After treatment, there was high statistically significant reduction in Vancouver Scar Scale among the three groups (P value ≤ 0.001), reduction was more in group II then I then III. Also, high statistically significant reduction in keloid scar thickness assessed by Doppler was recorded (P value ≤0.001 in group II and P value ≤0.01 in group I & III). Conclusion: Combined therapy is favorable in treatment of keloids. Trichloroacetic acid is a promising modality in treating keloid; hence it can be tried in different combinations. Color Doppler Ultrasound is a promising method of keloids pre- and post-treatment assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".