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Record W2901499106 · doi:10.5152/eurjrheum.2018.18121

Imaging in rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, and osteoarthritis: An international viewpoint on the current knowledge and future research priorities

2018· review· en· W2901499106 on OpenAlexaff
Xenofon Baraliakos, Philip G. Conaghan, Maria Antonietta D’Agostino, Walter P. Maksymowych, Esperanza Naredo, Georg Schett, Mikkel Østergaard, Paul Emery

Bibliographic record

VenueEuropean Journal of Rheumatology · 2018
Typereview
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsUniversity of Alberta
FundersLeeds Biomedical Research CentreAgence Nationale de la RechercheNational Institute for Health and Care Research
KeywordsMedicinePsoriatic arthritisSynovitisRheumatoid arthritisContext (archaeology)OsteoarthritisPsoriasisArthritisDermatologyAxial spondyloarthritisAnkylosing spondylitisPhysical therapyInternal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

Abstract Imaging is increasingly used in the routine management of rheumatic diseases as well as in the clinical trials of these disorders. This viewpoint, authored by a group of international imaging experts following two meetings dedicated to imaging in rheumatology, reports a consensus about the current knowledge and addresses where further research should be focused based on the views of the international imaging experts and discussion of the evidence with attending imaging practitioners. The goal was to maximize the potential of imaging to improve the clinical management of four rheumatic diseases. These rheumatic diseases include rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, and osteoarthritis. Cite this article as: Baraliakos X, Conaghan PG, D'Agostino MA, Maksymowych W, Naredo E, Ostergaard M, et al. Imaging in rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, and osteoarthritis: An international viewpoint on the current knowledge and future research priorities. Eur J Rheumatol 2019; 6(1): 37-45.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.048
GPT teacher head0.357
Teacher spread0.310 · 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 designOther design
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

Citations16
Published2018
Admission routes1
Has abstractyes

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