Pulsed dye laser in jellyfish-induced keloids
Bibliographic record
Abstract
Jellyfish stings can cause acute inflammatory skin lesions that may hesitate in keloids. Pulsed dye laser (PDL) represents one of the most effective treatments for newly developed keloids. Aim of this study was to evaluate the efficacy of PDL on newly developed keloids specifically induced by jellyfish stings in pediatric patients.We conducted a retrospective observational study on pediatric patients with newly developed keloids from jellyfish stings, treated in the last two years with 595 nm wavelength PDL with a duration of 0.45–1.5 msec, spot-size 7 mm and fluence 8.5–9.5 J/cm2. PDL therapy was administered for a mean of 7.4 treatment sessions, every 1–3 months. Two expert dermatologists evaluated the vascularity, pigmentation, height, and pliability of keloids, according to the Vancouver Scar Scale (VSS), pre-and-post treatment. A total of 17 patients (7 males, 10 females) were included in the study, mean age of 11 years. Overall, mean pre-treatment global VSS was 11.0 ± 1.50. After treatment, global VSS was 3.88 ± 1.87. At paired t-test, the difference between pre-treatment and post-treatment was highly statistically significant (p < .0001). Commonly, manipulation and therapeutic intervention on jellyfish scars and keloids is feared. The present study supports the use of PDL in keloids secondary to jellyfish stings, though conducted on a limited number of patients.
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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 | 0.001 |
| 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".