Efficacy and safety of fractional carbon dioxide laser followed by 5-aminolevulinic acid photodynamic therapy for keloids
Bibliographic record
Abstract
Keloids are aggressive fibroproliferative disorders that cause aesthetic and functional damage. Photodynamic therapy (PDT) has shown promise as a novel treatment for keloids. However, the limited penetration of 5-aminolevulinic acid (ALA) and unsatisfactory outcomes in dense scars hinder its effectiveness as a monotherapy. The objective of this study is to to assess the efficacy and safety of fractional CO2 laser followed by 5-ALA PDT for keloids. A total of 12 patients with keloid were included in our study. Each lesion was pretreated by fractional CO2 laser with 26-28 W to create microthermal zones. After topical application of 5-ALA solution, an irradiation of 635 nm red light with 120 J/cm2 was performed. The treatment was repeated at least every 2 weeks. Efficacy and safety were evaluated using the Vancouver Scar Scale (VSS), the Visual Analogue Scale (VAS) for keloid-related symptoms and documentation of postoperative complications. Statistical analysis was performed to compare VSS and keloid-related symptom VAS scores of the baseline and final treatment sessions. The final treatment resulted in a statistically significant decrease in all parameters of VSS and VAS for pruritus and pain compared to the baseline. Except for postoperative hyperpigmentation, no infections, scar aggravation, or recurrence were observed during at least 6 months of follow-up. Overall, patients expressed a high level of satisfaction with the treatment outcome. Fractional CO2 laser followed by 5-ALA PDT is a promising method for treating keloids. However, its synergetic effects need to be validated through clinical trials involving larger patient cohorts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".