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The OMERACT whole-body MRI scoring system for inflammation in peripheral joints and entheses (WIPE) in spondyloarthritis - reference image atlas for the knee region

2024· article· en· W4391513839 on OpenAlexaff
Mikkel Østergaard, Marie Wetterslev, A. E. F. Hadsbjerg, Walter P. Maksymowych, Iris Eshed, Lennart Jans, Yasser Emad, Susanne Juhl Pedersen, Maria Stoenoiu, Paul Bird, Violaine Foltz, Ashish Jacob Mathew, Joel Paschke, Philippe Carron, Gabriele De Marco, Helena Marzo‐Ortega, Signe Møller-Bisgaard, Philip G. Conaghan, R. Lambert

Bibliographic record

VenueSeminars in Arthritis and Rheumatism · 2024
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of Alberta
FundersMedacNational Institute for Health and Care ResearchCelgeneBiogenGilead SciencesAmgenPfizerEli Lilly and Company
KeywordsMedicineAtlas (anatomy)EnthesisSpondylarthritisWhole body imagingMagnetic resonance imagingRadiologyAnatomyAnkylosing spondylitisSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop a reference image atlas for the Outcome Measures in Rheumatology whole-body MRI scoring system for inflammation in peripheral joints and entheses (OMERACT MRI-WIPE) of the knee region. METHODS: Image examples of each pathology, location and grade, were collected and discussed at web-based, interactive meetings within the OMERACT MRI in Arthritis Working Group. Subsequently, reference images were selected by consensus. RESULTS: Reference images for each grade, pathology and location are depicted, along with definitions, reader rules and recommended MRI-sequences. CONCLUSION: The atlas guides scoring whole-body MRIs for inflammation in joints and entheses of the knee region according to MRI-WIPE methodology in clinical trials and cohorts.

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)0.015
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: Other · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.005

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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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
GenreOther

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

Citations3
Published2024
Admission routes1
Has abstractyes

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