The Scar Bane, Without the Pain: A New Approach in the Treatment of Elevated Scars: Thermomechanical Delivery of Topical Triamcinolone Acetonide and 5-Fluorouracil
Bibliographic record
Abstract
Keloids are challenging to treat due to their inadequate response to treatment and high recurrence rate. Intralesional triamcinolone acetonide (TAC) injection with or without 5-fluorouracil (5FU) is considered the first-line treatment for keloids. Three significant disadvantages of intralesional injections are the pain associated with the procedure, the uneven topography, and epidermal atrophy. Fractionated ablative carbon dioxide (CO 2 ) laser-assisted drug delivery (LADD) of the topical solution can help facilitate transdermal drug delivery and shows promise in scar remodeling. This study examined the use of a thermomechanical device (Tixel, Novoxel) to facilitate the transdermal delivery of TAC and 5-FU in the treatment of keloid scars. Seven patients each received eight topical thermal ablations, with one ablation performed every 2–3 weeks. TAC and 5FU were applied after each ablation. Outcomes were evaluated using the Vancouver Scar Scale (VSS), and pain was assessed using the Visual Analog Scale (VAS). Mean keloid VSS reduced from 8.6 ± 1.2 to 5 ± 2.7 after the eight treatments. Mean treatment pain VAS score was 2.4 ± 0.7. Patients rated their satisfaction level as moderate–high. No severe adverse reactions were noted. Thermomechanical drug delivery of TAC and 5-FU is safe and effective. This is a promising option for the treatment of keloid scars, particularly in the pediatric population.
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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 | 0.001 |
| 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".