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Record W4403991617 · doi:10.1080/14764172.2024.2420990

Pulsed dye laser in jellyfish-induced keloids

2024· article· en· W4403991617 on OpenAlexaboutno aff
Astrid Herzum, Gianmaria Viglizzo, Lodovica Gariazzo, Carlotta Pastorino, Nadia Casteni, C. Occella

Bibliographic record

VenueJournal of Cosmetic and Laser Therapy · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineJellyfishDermatologyLaserOpticsFishery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.016
GPT teacher head0.230
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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