Combined Therapeutic Strategies for Keloid Treatment
Bibliographic record
Abstract
BACKGROUND: Recent advances in keloid management favor the administration of combination therapy over monotherapy. OBJECTIVE: The authors evaluated the safety and efficacy of combination therapy to treat keloids using fractional lasers, cryotherapy, and intralesional corticosteroids. MATERIALS AND METHODS: The authors performed a retrospective study involving 35 Korean patients. Each patient underwent treatment using the 1,550 nm nonablative fractional erbium-glass laser, followed by the 10,600 nm ablative fractional carbon dioxide laser. Laser treatment was immediately followed by the administration of superficial cryotherapy and intralesional triamcinolone injection. Therapeutic efficacy was assessed using the Vancouver Scar Scale (VSS) score and the 7-point patient self-assessment score. RESULTS: The mean total and subcategory VSS scores showed statistically significant improvements. The height and pliability scores showed the most significant and quickest responses to the combination therapy. The patients reported remarkable improvement in itching, pain, and limitations of motion after a single combination therapy session. Twenty patients were followed up for 1 year after the discontinuation of the combination treatment, and the recurrence was observed only in one patient. No significant adverse effects were observed throughout the follow-up period. CONCLUSION: Combination keloid therapy using fractional lasers, superficial cryotherapy, and intralesional triamcinolone injection is safe and more effective than individual monotherapies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".