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Record W2099045655 · doi:10.1093/rheumatology/kei095

Developing a disease activity tool for systemic-onset juvenile idiopathic arthritis by international consensus using the Delphi approach

2005· article· en· W2099045655 on OpenAlexaff
Athimalaipet V Ramanan, Rayfel Schneider, Michelle Batthish, Camille Achonu, S. Ota, M. McLimont, Nancy L. Young, Brian M. Feldman

Bibliographic record

VenueLara D. Veeken · 2005
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersPfizer
KeywordsMedicineArthritisDelphi methodJuvenileDiseaseRashDelphiJuvenile rheumatoid arthritisPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The systemic form of juvenile idiopathic arthritis may present with many diverse symptoms, signs and laboratory abnormalities. Our aim was to elicit and pool items useful for developing a consensus disease activity measure for systemic arthritis in children, using an international pool of respondents. METHODS: We used a Delphi survey process in two steps. First we surveyed 187 paediatric rheumatologists and allied health professionals. We elicited 2607 items that, when combined with previously elicited items from parents/patients, could be pooled into 107 independent items. We then surveyed the paediatric rheumatologists to determine the frequency and importance of the 107 items. RESULTS: Our response rate was 83% to both surveys. We identified 29 items as being the most important and most frequently seen indicators of active disease. The most highly rated of these items were: presence of fever, presence of rash, elevated ESR, elevated CRP, requirement for increasing medications, abnormal physician global evaluation and presence of joints with active arthritis. CONCLUSIONS: Twenty-nine items are thought by medical practitioners to be most relevant in determining disease activity in systemic arthritis. As a next step, the measurement properties of these items will be tested to help develop a disease activity tool.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.041
GPT teacher head0.314
Teacher spread0.273 · 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 designNot applicable
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

Citations21
Published2005
Admission routes1
Has abstractyes

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