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Record W3108945525 · doi:10.1016/j.jdin.2020.10.007

A systematic review of vitiligo onset and exacerbation in patients receiving biologic therapy

2020· review· en· W3108945525 on OpenAlexaff
Muskaan Sachdeva, Asfandyar Mufti, Nadia Kashetsky, Jorge R. Georgakopoulos, Sheida Naderi-Azad, Jennifer Salsberg, Jensen Yeung

Bibliographic record

VenueJAAD International · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreProbity Medical ResearchMemorial University of NewfoundlandWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineVitiligoInternal medicineExacerbationProinflammatory cytokineDermatologyImmunologyInflammation

Abstract

fetched live from OpenAlex

To the Editor: Biologic therapies have improved outcomes in patients with immune-mediated inflammatory diseases due to their ability to inhibit specific proinflammatory cytokines, such as tumor necrosis factor-alfa (TNF-α) and interleukins (IL).1 An infrequent side effect of biologic therapy is the onset of vitiligo. This systematic review aimed to comprehensively summarize the existing literature on new-onset and exacerbations of vitiligo after biologic use.

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)0.010
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0100.004
Bibliometrics0.0120.013
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.020
GPT teacher head0.316
Teacher spread0.296 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2020
Admission routes1
Has abstractyes

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