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Record W4292839950 · doi:10.1002/art.42329

Development and External Validation of a Model Predicting <scp>New‐Onset</scp> Chronic Uveitis at Different Disease Durations in Juvenile Idiopathic Arthritis

2022· article· en· W4292839950 on OpenAlexfundno aff
Joeri W. van Straalen, Lianne Kearsley‐Fleet, Jens Klotsche, Sytze de Roock, Kirsten Minden, Arnd Heiligenhaus, Kimme L Hyrich, Joke H. de Boer, Lovro Lamot, Alma Nunzia Olivieri, Romina Gallizzi, Elżbieta Smolewska, Enrique Faugier Fuentes, Serena Pastore, Philip J. Hashkes, Cristina Herrera, Wolfgang Emminger, Rita Consolini, Nico Wulffraat, Nicolino Ruperto, Joost F. Swart

Bibliographic record

VenueArthritis & Rheumatology · 2022
Typearticle
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsnot available
FundersCentre for Epidemiology Versus Arthritis, University of ManchesterManchester Biomedical Research CentreFoundation for Research in RheumatologyVersus ArthritisBundesministerium für Bildung und ForschungEuropean CommissionHospital for Sick ChildrenUniversity of ManchesterUniversity College LondonNational Institute for Health and Care ResearchAlder Hey Children's NHS Foundation Trust
KeywordsMedicineOligoarthritisPsoriatic arthritisInternal medicineHazard ratioCohortArthritisProportional hazards modelUveitisConfidence intervalPhysical therapyPolyarthritisImmunology

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop and externally validate a prediction model for new-onset chronic uveitis in children with juvenile idiopathic arthritis (JIA) for clinical application. METHODS: Data from the international Pharmachild registry were used to develop a multivariable Cox proportional hazards model. Predictors were selected by backward selection, and missing values were handled by multiple imputation. The model was subsequently validated and recalibrated in 2 inception cohorts: the UK Childhood Arthritis Prospective Study (CAPS) study and the German Inception Cohort of Newly diagnosed patients with juvenile idiopathic arthritis (ICON) study. Model performance was evaluated by calibration plots and C statistics for the 2-, 4-, and 7-year risk of uveitis. A diagram and digital risk calculator were created for use in clinical practice. RESULTS: A total of 5,393 patients were included for model development, and predictor variables were age at JIA onset (hazard ratio [HR] 0.83 [95% confidence interval (95% CI) 0.77-0.89]), ANA positivity (HR 1.59 [95% CI 1.06-2.38]), and International League of Associations for Rheumatology category of JIA (HR for oligoarthritis, psoriatic arthritis, and undifferentiated arthritis versus rheumatoid factor-negative polyarthritis 1.40 [95% CI 0.91-2.16]). Performance of the recalibrated prediction model in the validation cohorts was acceptable; calibration plots indicated good calibration and C statistics for the 7-year risk of uveitis (0.75 [95% CI 0.72-0.79] for the ICON cohort and 0.70 [95% CI 0.64-0.76] for the CAPS cohort). CONCLUSION: We present for the first time a validated prognostic tool for easily predicting chronic uveitis risk for individual JIA patients using common clinical parameters. This model could be used by clinicians to inform patients/parents and provide guidance in choice of uveitis screening frequency and arthritis drug therapy.

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)0.028
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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 designSimulation or modeling
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

Citations9
Published2022
Admission routes1
Has abstractyes

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