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Record W4416767637 · doi:10.1111/jocd.70567

Prospective Study of Intense Pulsed Light With Postoperative Radiotherapy for Keloids in Young and Middle‐Aged Men

2025· article· en· W4416767637 on OpenAlexaboutno aff
Qiaoling Weng, Junjun Sang, Jie Wu, Wen Xu, Shengping Chen, Xiangqi Chen

Bibliographic record

VenueJournal of Cosmetic Dermatology · 2025
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsIntense pulsed lightRadiation therapyProspective cohort studyItchingKeloid

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the efficacy of using a combination of intense pulsed light (IPL) with a 500-600 nm wavelength filter and postoperative radiotherapy to treat chest scars in young and middle-aged men. METHODS: A prospective randomized controlled trial was conducted with 52 male patients. Those receiving postoperative radiotherapy combined with IPL were assigned to the observation group, while those receiving only postoperative radiotherapy were assigned to the control group. Follow-ups were conducted from 0 to 24 months to assess efficacy using the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS). RESULTS: No significant differences in VSS or POSAS scores were observed prior to treatment (p > 0.05). Following treatment, however, the observation group demonstrated significantly superior outcomes (p < 0.001), exhibiting lower scores in terms of color, vascular distribution, softness and total VSS (p < 0.05), though thickness scores remained unchanged (p > 0.05). Pain and itchiness scores were also significantly lower in the observation group. CONCLUSION: Combining postoperative radiotherapy with IPL is more effective than radiotherapy alone at improving chest scars, and significantly alleviates pain and itching symptoms in young and middle-aged men.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.320
Teacher spread0.306 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

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