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Record W4414072698 · doi:10.1155/dth/6693871

Optimizing Laser Therapy: Efficacy and Safety of Picosecond 1,064 nm Nd:YAG Laser in Xanthelasma Palpebrarum Treatment

2025· article· en· W4414072698 on OpenAlexaff
Fakültesi Su, Luoyao Yang, Qinsi Huang, Hai Jun Su, Liuqing Chen

Bibliographic record

VenueDermatologic Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsInstitute of Infection and Immunity
FundersHealth Commission of Hubei Province
KeywordsPicosecondLaserAdverse effectLesionLaser treatmentHyperpigmentation

Abstract

fetched live from OpenAlex

Background: Xanthelasma palpebrarum (XP) currently lacks a universally endorsed treatment approach. The utility of picosecond 1064 nm Nd:YAG lasers in XP management has not been explored. Objective: This study aimed to assess the efficacy and safety of picosecond 1064 nm Nd:YAG laser therapy in the treatment of XP. Methods: A total of 47 patients with clinically confirmed XP received treatment with picosecond 1064 nm Nd:YAG laser at standardized settings. Patients were photographed using standardized photographic documentation, and the degree of clearance was evaluated. Results: Following the initial treatment session, 97.87% of patients exhibited a good response to the picosecond laser, with some degree of lesion clearance. By the third session, 78.80% of patients achieved > 50% lesion clearance. Adverse effects were mild and transient, with postinflammatory hyperpigmentation observed in 2 (4.25%) patients. Conclusion: The picosecond 1064 nm Nd:YAG laser demonstrated promising efficacy in the treatment of XP, with a potentially enhanced safety profile.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.031
GPT teacher head0.321
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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
Published2025
Admission routes1
Has abstractyes

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