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Record W2910865966 · doi:10.3899/jrheum.181062

The 2018 OMERACT Synovial Tissue Biopsy Special Interest Group Report on Standardization of Synovial Biopsy Analysis

2019· article· en· W2910865966 on OpenAlexvenueno aff
Mihir D. Wechalekar, Aurélie Najm, Douglas J. Veale, Vibeke Strand

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatoid arthritisDelphi methodBiopsyRheumatologyInternal medicineDelphiMulticenter studyPhysical therapyStandardizationSurgeryRandomized controlled trial

Abstract

fetched live from OpenAlex

OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) synovial tissue biopsy (STB) working group initiated an international effort to standardize STB analyses, define consensual items to inform treatment choices, and predict responses in rheumatoid arthritis (RA). METHODS: (1) A Delphi survey to determine items for STB analyses. (2) A multicenter retrospective study of STB data in patients with RA posttreatment with biological disease-modifying antirheumatic drugs. RESULTS: The Delphi survey identified 18 STB analyses items. Consensus on histological markers was achieved in the OMERACT 2018 SIG. CONCLUSION: Six markers were identified for examination in a multicenter study designed to define an OMERACT-endorsed set of STB markers to predict responses to treatment.

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)0.117
metaresearch head score (Gemma)0.126
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score0.617

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1170.126
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations7
Published2019
Admission routes1
Has abstractyes

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