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Hip and pelvis region MRI reference image atlas for scoring inflammation in peripheral joints and entheses according to the OMERACT-MRI WIPE scoring system in patients with spondyloarthritis

2024· article· en· W4391470240 on OpenAlexaff
Mikkel Østergaard, R. Lambert, A. E. F. Hadsbjerg, Iris Eshed, Walter P. Maksymowych, Ashish Jacob Mathew, Lennart Jans, Susanne Juhl Pedersen, Philippe Carron, Yasser Emad, Gabriele De Marco, Paul Bird, Maria Stoenoiu, Violaine Foltz, Joel Paschke, Helena Marzo‐Ortega, Signe Møller-Bisgaard, Philip G. Conaghan, Marie Wetterslev

Bibliographic record

VenueSeminars in Arthritis and Rheumatism · 2024
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsUniversity of Alberta
FundersLeeds Biomedical Research CentreNational Institute for Health and Care Research
KeywordsMedicinePelvisEnthesisAtlas (anatomy)RadiologyHip painMagnetic resonance imagingNuclear medicineSurgeryAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop a reference image atlas for scoring the hip/pelvis region according to the OMERACT whole-body MRI scoring system for inflammation in peripheral joints and entheses (MRI-WIPE). METHODS: We collected image examples of each pathology, location and grade, discussed them at web-based, interactive meetings and, finally, selected reference images by consensus. RESULTS: Reference images for each grade and location of osteitis, synovitis and soft tissue inflammation are provided, as are definitions, reader rules and recommended MRI-sequences. CONCLUSION: A reference image atlas was created to guide scoring whole-body MRIs for arthritis and enthesitis in the hip/pelvis region in spondyloarthritis/psoriatic arthritis 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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.005

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 head0.010
GPT teacher head0.241
Teacher spread0.230 · 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 designObservational
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

Citations2
Published2024
Admission routes1
Has abstractyes

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