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Record W4220935912 · doi:10.1002/art.42113

A Comparison of International League of Associations for Rheumatology and Pediatric Rheumatology International Trials Organization Classification Systems for Juvenile Idiopathic Arthritis Among Children in a Canadian Arthritis Cohort

2022· article· en· W4220935912 on OpenAlexafffundabout
Jennifer J. Lee, Simon Eng, Jaime Guzmán, Ciarán M. Duffy, Lori B. Tucker, Kiem Oen, Rae S. M. Yeung, Brian M. Feldman

Bibliographic record

VenueArthritis & Rheumatology · 2022
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsUniversity of ManitobaChildren's Hospital of Eastern OntarioUniversity of OttawaUniversity of British ColumbiaSickKids FoundationBC Children's HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineInternal medicineRheumatologyArthritisCohort

Abstract

fetched live from OpenAlex

Objective The aim of the Paediatric Rheumatology International Trials Organisation (PRINTO) juvenile idiopathic arthritis (JIA) classification criteria, which is still in development, is to identify homogeneous groups of JIA patients. This study was undertaken to compare International League of Associations for Rheumatology (ILAR) JIA classification criteria and PRINTO JIA classification criteria using data from the ReACCh‐Out (Research in Arthritis in Canadian Children, Emphasizing Outcomes) cohort. Methods We used clinicobiologic data recorded within 7 months of diagnosis to assign a diagnosis of JIA and identify subcategories of JIA among 1,228 patients according to the 2 JIA classification systems. We compared the proportions of patients classified and the alignment of classification categories with clinicobiologic subtypes and adult arthritis types. Results The PRINTO criteria classified 244 patients (19.9%) as having early‐onset antinuclear antibody–positive JIA, 157 (12.8%) as having enthesitis/spondylitis–related JIA, 38 (3.1%) as having systemic JIA, and 10 (0.8%) as having rheumatoid factor–positive JIA. A total of 12% of patients were unclassifiable using the ILAR criteria, while 63.3% were unclassifiable using the PRINTO criteria (777 with other JIA and 2 with unclassified JIA). In sensitivity analyses, >50% of patients remained unclassifiable using the PRINTO criteria. Compared to the PRINTO criteria, ILAR JIA categories aligned better with clinicobiologic subtypes in 131 patients (χ2 = 44, P = 0.005, versus χ2 = 15, P = 0.07 for PRINTO), and ILAR categories aligned better with adult types of arthritis in 389 evaluable patients. Conclusion Currently identified PRINTO disorders can only be used to classify a minority of JIA patients, leaving a large proportion of JIA patients with other disorders requiring further characterization. Current PRINTO JIA classification criteria do not align better with clinicobiologic subtypes or adult forms of arthritis compared with the older ILAR classification system.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.095
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.312
Teacher spread0.288 · 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.

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".

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Citations23
Published2022
Admission routes3
Has abstractyes

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