Treatment of hypertrophic scars and keloids with an LBO laser (532 nm) and silicone gel sheeting
Bibliographic record
Abstract
BACKGROUND: Keloid scars continue to be a complex and poorly understood subject. The main problem faced by researchers is the lack of an animal model because keloids affect only humans. Traditional techniques for keloids and hypertrophic scars are still available. More recently, lasers have gained an increasing role in the treatment of hypertrophic scars and keloids. METHODS: A total of 37 consecutive patients (31 females and six males; F:M=5:1 ratio) with 48 scars (34 hypertrophic and 14 keloids) were included in this study. Patients ranged in age from 8 to 67 years (mean age 34 years) with Fitzpatrick skin types II-IV. The age of scars ranged from 3 to 35 months (average 9 months). The scars were classified according to the Vancouver Scars Scale (VSS). Clinical digital photography was performed under standard and cross-polarized illumination. Laser treatment was performed in association with silicone gel sheeting. RESULTS: Overall, excellent resolution of the scars was achieved, with an initial average VSS score of 12.6 and a mean VSS final score of 3.3. CONCLUSION: The combined use of silicone gel sheeting and a 532-nm millisecond laser is an effective and safe treatment for hypertrophic scars and keloids.
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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".