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Record W4221106322 · doi:10.3899/jrheum.211340

What Constitutes a Positive MRI for Clinical Trial Recruitment of Psoriatic Arthritis Patients With Axial Involvement?

2022· review· en· W4221106322 on OpenAlexaffvenue
Walter P. Maksymowych, Mikkel Østergaard

Bibliographic record

VenueThe Journal of Rheumatology · 2022
Typereview
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicinePsoriatic arthritisPsoriasisAxial spondyloarthritisMagnetic resonance imagingArthritisClinical trialRandomized controlled trialInflammationRadiologyPhysical therapySurgeryInternal medicineDermatologySacroiliitis

Abstract

fetched live from OpenAlex

There has been a resurgence of interest in defining the axial inflammation component of psoriatic arthritis (PsA) since recent randomized controlled trials (RCTs) raised the possibility that this entity may respond differentially to therapeutics compared to patients with axial spondyloarthritis. A workshop was conducted during the 2021 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis annual meeting to review the literature on diagnosing PsA and to determine which criteria might be most appropriate. There was quite strong agreement that magnetic resonance imaging (MRI) had an important role to play in helping to define axial inflammation in PsA and that a data-driven methodology for generating optimal MRI quantitative cut-offs for lesions in the sacroiliac joints and/or spine that reflect imaging typical of axial inflammation in PsA would be most desirable.

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)0.057
metaresearch head score (Gemma)0.125
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.057
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.125
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.148
GPT teacher head0.405
Teacher spread0.257 · 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 designNot applicable
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
Published2022
Admission routes2
Has abstractyes

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