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Record W4411712184 · doi:10.1093/bjd/ljaf085.094

P066 Core outcome set implementation in vitiligo clinical trials: barriers and facilitators from a survey of lead researchers

2025· article· en· W4411712184 on OpenAlexaff
Shahnawaz Towheed, Viktoria Eleftheriadou, Khaled Ezzedine

Bibliographic record

VenueBritish Journal of Dermatology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVitiligoOutcome (game theory)Core (optical fiber)Set (abstract data type)MedicineClinical trialMedical physicsDermatologyComputer sciencePathology

Abstract

fetched live from OpenAlex

Abstract Core outcome sets (COSs) standardize outcome measures across clinical trials, ensuring consistency and comparability of results. In 2015, an internationally agreed COS for vitiligo randomized clinical trials (RCTs) identified three essential outcomes: repigmentation, side-effects/harms, and maintenance of gained repigmentation. Despite its significance, adoption of the COS across vitiligo trials has been inconsistent. This study evaluated COS uptake, barriers to implementation, and facilitators. Eighty-five authors of vitiligo RCTs between 2016 and 2021 were identified through a systematic review evaluating COS uptake, and were contacted via email. Recruitment occurred between July and November 2024, and 36 authors completed the Google Forms survey (response rate 42%). Respondents, predominantly dermatologists worldwide, described awareness and integration of COS domains within their trials. Quantitative results revealed that all respondents incorporated repigmentation as an outcome, and 97% assessed side-effects/harms. However, maintenance of gained repigmentation was measured by only 64% of respondents. Among the four recommended outcomes, 67% of respondents evaluated cosmetic acceptability and quality of life, while tolerability/burden of treatment and cessation of spreading were assessed less frequently (50% and 47%, respectively). Thematic analysis identified barriers to COS implementation. The most cited challenge (44%) concerned long-term follow-up to assess maintenance of gained repigmentation, with many noting cost and resource constraints. Additionally, 28% highlighted the impracticality of assessing maintenance in early-phase trials, where efficacy was the primary focus – despite only late-phase trials being surveyed. Ambiguity in defining certain domains, such as cessation of spreading, was reported by 19%, particularly in trials of stable vitiligo. Some respondents (8%) noted that repetitive data collection burdened patients and trial staff, contributing to incomplete datasets. To enhance uptake, respondents recommended increasing awareness of COS outcomes through public campaigns and academic presentations (53%). Simplifying and validating measurement tools for COS outcomes was suggested by 36%, alongside calls for journal reviewers to require COS adherence during manuscript submission (32%). A tiered outcome collection approach was also proposed, prioritizing repigmentation and side-effects in initial trials, with maintenance in long-term follow-ups. This evaluation underscores the need for flexible COS implementation reflecting practical challenges of vitiligo RCTs. While adoption of repigmentation and side-effects measures reflects COS awareness, gaps in assessing maintenance of repigmentation highlight persistent barriers. Given that most vitiligo RCTs span 52–54 weeks, yet 40% of patients relapse within 3–4 months post-treatment, tracking long-term maintenance is vital. Addressing logistical and definitional challenges, refining outcome frameworks, and fostering collaboration between researchers, industry and journal reviewers will be essential to facilitate COS adherence. These findings will inform ongoing COS refinement and support a planned complementary survey of dermatology journal editors to evaluate COS uptake at the publication level.

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)0.026
metaresearch head score (Gemma)0.047
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.597
GPT teacher head0.648
Teacher spread0.051 · 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
Published2025
Admission routes1
Has abstractyes

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