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Record W2554064357 · doi:10.1111/pcmr.12561

Repigmentation in vitiligo: position paper of the Vitiligo Global Issues Consensus Conference

2016· article· en· W2554064357 on OpenAlexaff
Emily Yiping Gan, Viktoria Eleftheriadou, Samia Esmat, Iltefat Hamzavi, Thierry Passeron, Markus Böhm, Tag S. Anbar, Boon Kee Goh, Cheng‐Che E. Lan, Harvey Lui, M Ramam, Noufal Raboobee, Ichiro Katayama, Tamio Suzuki, Davinder Parsad, Vaneeta Seth, Henry W. Lim, Nanja van Geel, Sanjeev V. Mulekar, John E. Harris, Richard A. Wittal, Laïla Benzekri, Yvon Gauthier, Prasad Kumarasinghe, Steven Tien Guan Thng, Caio César Silva de Castro, Marwa Abdallah, Charlotte Vrijman, Marcel W. Bekkenk, Julien Sénéschal, Amit G. Pandya, Khaled Ezzedine, Mauro Picardo, Alain Taı̈eb

Bibliographic record

VenuePigment Cell & Melanoma Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsVitiligoMedicineDelphi methodDermatologyConsensus conferenceDelphiComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The Vitiligo Global Issues Consensus Conference (VGICC), through an international e-Delphi consensus, concluded that 'repigmentation' and 'maintenance of gained repigmentation' are essential core outcome measures in future vitiligo trials. This VGICC position paper addresses these core topics in two sections and includes an atlas depicting vitiligo repigmentation patterns and color match. The first section delineates mechanisms and characteristics of vitiligo repigmentation, and the second section summarizes the outcomes of international meeting discussions and two e-surveys on vitiligo repigmentation, which had been carried out over 3 yr. Treatment is defined as successful if repigmentation exceeds 80% and at least 80% of the gained repigmentation is maintained for over 6 months. No agreement was found on the best outcome measure for assessing target or global repigmentation, therefore highlighting the limitations of e-surveys in addressing clinical measurements. Until there is a clear consensus, existing tools should be selected according to the specific needs of each study. A workshop will be conducted to address the remaining issues so as to achieve a consensus.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.034
GPT teacher head0.351
Teacher spread0.317 · 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 designBench or experimental
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

Citations53
Published2016
Admission routes1
Has abstractyes

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