Fractional Laser As Laser Assisted Drug Delivery of Triamcinolone Acetonide inKeloid
Bibliographic record
Abstract
Background: Keloid is benign hyperplasia of dermal collagen which may or may not be preceded by injury in susceptible person. Keloids are refractory to treatment most of the times. Intralesional corticosteroid, topical retinoic acid, topical imiquimod cream, surgery, cryotherapy, laser, and silicon sheeting are mainly used for treatment. Fractional ablative laser is a new laser treatment modality that create numerous microscopic thermal injury zone controlled width, depth, and density that are surrounded by a reservoir of spared epidermal and dermal tissue, allowing of rapid repair of laser-induced thermal injury. Multiple studies demonstrate that laser pretreatment of the skin can increase the permeability and depth of penetration of topical drug molecules. Main observations: A boy, 12 years, scar that arise after burn scar 14 months ago. Scar was felt bigger and thickening also itching. Patient was diagnosed keloid and had been treated with same-session ablative fractional laser and topical triamcinolone acetonide after therapy. Patient had been treated 5 sessions with 3 weeks of interval. Successful of treatment was measured with reduction of keloid size and vancouver scar scale (VSS). Conclusions: Laser assisted drug delivery is an envolving technology with potentially broad clinical application. Ablative fractional laser treatment create vertical channels that might assist the delivery of drug into skin. Combination same-session therapy with ablative fractional laser and triamcinolone acetonide offer a good combination caused assisted delivery of drug.
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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) | 0.002 | 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".