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Record W2138185297 · doi:10.1136/ard.2008.092353

Methods of deriving EULAR/ACR recommendations on reporting disease activity in clinical trials of patients with rheumatoid arthritis

2008· article· en· W2138185297 on OpenAlexaff
Thomas Karonitsch, Daniel Aletaha, Maarten Boers, Stefano Bombardieri, Bernard Combe, Maxime Dougados, Paul Emery, David T. Felson, Juan J. Gómez‐Reino, Ed Keystone, Tore K. Kvien, E. Martín‐Mola, Marco Matucci‐Cerinic, Patricia Richards, P. Van Riel, J. Siegel, Josef S Smolen, Tuulikki Sokka, D. van der Heijde, Ronald van Vollenhoven, Michael M. Ward, George A. Wells, A. Zink, Robert Landewé

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2008
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of OttawaUniversity of Toronto
FundersVersus Arthritis
KeywordsMedicineRheumatismRheumatologyRheumatoid arthritisMEDLINECochrane LibraryClinical trialPhysical therapySystematic reviewDiseaseEvidence-based medicineInternal medicineAlternative medicineIntensive care medicineRandomized controlled trialPathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.587
metaresearch head score (Gemma)0.825
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.413
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5870.825
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0090.011
Bibliometrics0.0170.013
Science and technology studies0.0020.003
Scholarly communication0.0140.004
Open science0.0080.008
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0070.002

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 head0.214
GPT teacher head0.477
Teacher spread0.263 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
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

Citations22
Published2008
Admission routes1
Has abstractno

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