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Record W2135228270 · doi:10.3899/jrheum.090354

The OMERACT Ultrasound Task Force — Advances and Priorities

2009· article· en· W2135228270 on OpenAlexvenueaboutno aff
Maria Antonietta D’Agostino, Philip G. Conaghan, Esperanza Naredo, Philippe Aegerter, Annamaria Iagnocco, Jane Freeston, Emilio Filippucci, Ingrid Möller, Carlos Pineda, Marina Backhaus, Helen Keen, GURJEET KAELEY, HANS-RUDOLF ZISWEILER, Wolfgang Schmidt, Péter Bálint, George A. W. Bruyn, Sandrine Jousse‐Joulin, David Kane, Marcin Szkudlarek, Lene Terslev, Richard J. Wakefield

Bibliographic record

VenueThe Journal of Rheumatology · 2009
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTask forcePhysical therapyReliability (semiconductor)Medical physicsPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

This article reports the most recent work of the OMERACT Ultrasound Task Force (post OMERACT 8) and highlights of future research priorities discussed at the OMERACT 9 meeting, Kananaskis, Canada, May 2008. Results of 3 studies were presented: (1) assessing intermachine reliability; (2) applying the scoring system developed in the hand to other joints most commonly affected in rheumatoid arthritis (RA); and (3) assessing interobserver reliability on a deep target joint (shoulder). Results demonstrated good intermachine reliability between multiple examiners, and good applicability of the scoring system for the hand on other joints (including shoulder). Study conclusions were discussed and a future research agenda was generated, notably the further development of a Global OMERACT Sonography Scoring (GLOSS) system in RA, emphasizing the importance of testing feasibility and added value over standard clinical variables. Future disease areas of importance to develop include a scoring system for enthesitis and osteoarthritis.

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)0.089
metaresearch head score (Gemma)0.056
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 score0.089
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0890.056
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.006
Science and technology studies0.0020.002
Scholarly communication0.0060.007
Open science0.0040.005
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0060.004

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 head0.007
GPT teacher head0.275
Teacher spread0.268 · 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

Citations78
Published2009
Admission routes2
Has abstractyes

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