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Record W2773287496 · doi:10.3899/jrheum.170501

The Use of the OMERACT Ultrasound Tenosynovitis Scoring System in Multicenter Clinical Trials

2017· article· en· W2773287496 on OpenAlexvenueno aff
Mads Ammitzbøll‐Danielsen, Mikkel Østergaard, Esperanza Naredo, Annamaria Iagnocco, Ingrid Möller, Maria Antonietta D’Agostino, Frédérique Gandjbakhch, Lene Terslev

Bibliographic record

VenueThe Journal of Rheumatology · 2017
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTenosynovitisClinical trialMulticenter studyInternal medicineMulticenter trialRheumatologyPhysical therapySurgeryRandomized controlled trial

Abstract

fetched live from OpenAlex

OBJECTIVE: To test the sensitivity to change of the Outcome Measures in Rheumatology Clinical Trials (OMERACT) ultrasound (US) scoring system for tenosynovitis when applied in a multicenter design. METHODS: RA patients with US-verified tenosynovitis were recruited when scheduled for treatment intensification. Tenosynovitis was assessed at baseline, and 3 and 6 months followup, using the semiquantitative OMERACT scoring system. RESULTS: Expressed in median (25th; 75th percentiles), the overall greyscale and Doppler score decreased significantly from baseline at 4 (2; 7) and 3 (2; 6), to 6 months at 2 (0; 3) and 0 (0; 1, p < 0.01), respectively, and showed high responsiveness (standardized response mean ≥ 0.8). CONCLUSION: The OMERACT US scoring system for tenosynovitis showed high responsiveness, supporting its use for diagnosing and monitoring tenosynovitis in multicenter trials.

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)0.200
metaresearch head score (Gemma)0.272
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2000.272
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.415
Teacher spread0.259 · 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 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

Citations14
Published2017
Admission routes1
Has abstractyes

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