MétaCan
Menu
Back to cohort
Record W2414151108

Validation of the proposed ILAR classification criteria for juvenile idiopathic arthritis. International League of Associations for Rheumatology.

2000· article· en· W2414151108 on OpenAlexaboutno aff
Ivan Foeldvari, M Bidde

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatologyPsoriatic arthritisInternal medicinePsoriasisArthritisPopulationPhysical therapyRheumatoid arthritisDermatology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: The new International League of Associations for Rheumatology (ILAR) classification criteria were proposed to facilitate communication among pediatric rheumatologists. Before they are applied in daily practice they should be clinically validated. METHODS: We retrospectively applied the proposed criteria on our pediatric rheumatology patient population seen between June 1 and August 31, 1998. RESULTS: We saw 67 patients with oligoarticular (oJRA), 6 with polyarticular/RF negative (pJRA), and 8 with systemic juvenile rheumatoid arthritis (sJRA), all classified according to American College of Rheumatology criteria, 5 with juvenile psoriatic arthritis (PsA) according to the Vancouver criteria, and 11 with juvenile spondyloarthritis (SP) according to the European Spondylarthropathy Study Group preliminary criteria. Of the 97 patients, 85 could be clearly classified according to the ILAR criteria. Twelve patients (12%) were classified as "other." Six patients could not be classified as "oligo" because of a family history of psoriasis, and did not fulfill the criteria for PsA either. All 6 "other" patients fulfilled criteria for 2 different categories. CONCLUSION: With this ILAR proposed classification criteria 88% of patients could be classified. In patients classified as "other," the psoriatic trait caused the most difficulty in classification.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.046
GPT teacher head0.305
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 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
Published2000
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicAutoimmune and Inflammatory Disorders ResearchFrench-language works237,207