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Record W2009307145 · doi:10.3899/jrheum.090371

Progress in Measurement Instruments for Acute and Chronic Gout Studies

2009· article· en· W2009307145 on OpenAlexvenueno aff
Rebecca Grainger, William J. Taylor, Nicola Dalbeth, Fernando Pérez-Ruiz, Jasvinder A. Singh, Royce W. Waltrip, Naomi Schlesinger, Robert R. Evans, N. Lawrence Edwards, Francisca Sivera, Cèsar Díaz‐Torné, Patricia MacDonald, Fiona McQueen, H. Ralph Schumacher

Bibliographic record

VenueThe Journal of Rheumatology · 2009
Typearticle
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGoutTophusPhysical therapyContext (archaeology)Quality of life (healthcare)Intensive care medicineInternal medicineHyperuricemiaUric acid

Abstract

fetched live from OpenAlex

Consensus exercises have identified and prioritized domains of measurement for studies in acute and chronic gout. In parallel, the technical properties of instruments for measurement in many of these domains have been assessed, with the main objective to consider the instruments in the context of the OMERACT filter of truth, discrimination, and feasibility. These data were presented and discussed at OMERACT 9 in the gout workshop, in breakout groups, and at informal meetings of the gout group. In acute gout, instruments for domains of pain, joint swelling, joint tenderness, and patient and physician global assessment have been assessed. In chronic gout, some validation exercises have been performed in instruments for domains serum urate, tophus measurement, health-related quality of life (HRQOL). In voting at OMERACT 9, the Medical Outcomes Study Short-Form 36 was endorsed as a valid instrument for measurement of HRQOL. Methods of tophus measurement were considered to have met some criteria of the OMERACT filter, but these require further work, particularly regarding sensitivity to change over shorter time periods. Priorities for future research include measurement of joint inflammation in acute gout and disability in acute and chronic gout.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.335
Teacher spread0.299 · 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 teacher head, 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

Citations56
Published2009
Admission routes1
Has abstractyes

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