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Record W3005675677 · doi:10.1136/rmdopen-2019-001150

Atlas of the OMERACT Heel Enthesitis MRI Scoring System (HEMRIS)

2020· article· en· W3005675677 on OpenAlexaff
Ashish Jacob Mathew, Simon Krabbe, Iris Eshed, R. Lambert, Jean‐Denis Laredo, Walter P. Maksymowych, Frédérique Gandjbakhch, Yasser Emad, Maria Stoenoiu, Violaine Foltz, Paul Bird, Philippe Carron, Joel Paschke, Philip G. Conaghan, Susanne Juhl Pedersen, Daniel Glinatsi, Mikkel Østergaard

Bibliographic record

VenueRMD Open · 2020
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsResearch CanadaUniversity of Alberta
FundersLeeds Biomedical Research CentreNational Institute for Health and Care Research
KeywordsMedicineAtlas (anatomy)EnthesitisHeelNuclear medicineInternal medicineArthritisAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: Assessment of enthesitis, a key feature in spondyloarthritis (SpA) and psoriatic arthritis (PsA), using objective and sensitive methods is pivotal in clinical trials. MRI allows detection of both soft tissue and intra-osseous changes of enthesitis. This article presents an atlas for the Outcome Measures in Rheumatology (OMERACT) Heel Enthesitis Magnetic Resonance ImagingMRI Scoring System (HEMRIS). METHODS: Following a preliminary selection of potential examples of each grade, as per HEMRIS definitions, the images along with detailed definitions and reader rules were discussed at web-based, interactive meetings between the members of the OMERACT MRI in Arthritis Working Group. RESULTS: Reference images of each grade of the MRI features to be assessed using HEMRIS, along with reader rules and recommended MRI sequences are depicted. CONCLUSION: The presented reference images can be used to guide scoring Achilles tendon and plantar fascia (plantar aponeurosis) enthesitis according to the OMERACT HEMRIS in clinical trials and cohorts in which MRI enthesitis is used as an outcome.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.006

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 head0.034
GPT teacher head0.281
Teacher spread0.246 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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