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Record W2008634258 · doi:10.1002/art.38424

A13: The Research in Arthritis in Canadian Children Emphasizing Outcomes (ReACCh Out) Cohort: Prospective Determination of the Incidence of New Onset Uveitis in Juvenile Idiopathic Arthritis

2014· article· en· W2008634258 on OpenAlexaffabout
Karen N. Watanabe Duffy, Jennifer Lee, Jaime Guzmán, Nick Barrowman, Kimberly Morishita, Lynn Spiegel, Elizabeth Stringer, Michele Gibbon, Rae S. M. Yeung, Lori B. Tucker, Kiem Oen, Ciarán M. Duffy

Bibliographic record

VenueArthritis & Rheumatology · 2014
Typearticle
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsUniversity of ManitobaIzaak Walton Killam Health CentreUniversity of TorontoBC Children's HospitalChildren's Hospital of WinnipegHospital for Sick ChildrenUniversity of British ColumbiaChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineUveitisIncidence (geometry)Prospective cohort studyPediatricsArthritisCohortCohort studyRate ratioConfidence intervalInternal medicineOphthalmology

Abstract

fetched live from OpenAlex

Background/Purpose: Previous studies of uveitis in Juvenile Idiopathic Arthritis (JIA) patients have reported prevalence and not incidence. The ReACCh Out cohort, a large inception cohort of newly diagnosed JIA patients provided the opportunity to prospectively ascertain the true incidence of new onset uveitis. The objectives of this study were to determine the overall incidence rate and its trajectory over time. Methods: ReACCh Out recruited newly diagnosed JIA patients between January 2005 and December 2010, from 16 Canadian centres across the country. Prospective data was collected every 6 months for the first 2 years, then yearly. Data was collected on numerous clinical and laboratory measures including the diagnosis of uveitis and its complications, determined by an ophthalmologist. A Poisson model was used to estimate the overall incidence rate. A Kaplan‐Meier plot was used to evaluate the time from diagnosis of JIA to the time of diagnosis of new onset uveitis. Results: 1104 patients with newly diagnosed (≤6 months) JIA with ≥1 follow‐up visit were reviewed. Patients were predominantly female (63%), age at diagnosis was 9.3 (3.9, 13.0) years. Time from diagnosis to enrollment was 0.3 (0, 1.6) months. Follow‐up to last visit or study end was 34.2 (21.5, 48) months. 23 patients whose uveitis status was not available, were excluded. 77 patients with new onset uveitis were identified during the study period. The overall incidence rate of new cases of uveitis following the diagnosis of JIA was 2.9% per year (95% confidence interval 2.3–3.6). Following the trajectory of new cases of uveitis over time, the incidence of new cases showed a slow decline over time (Figure ). Importantly, new cases of uveitis occurred as far out from diagnosis as the end of the study period. Kaplan‐Meier plots were also used to evaluate age at diagnosis of new onset uveitis and gender. Results support previously identified risk factors for uveitis including younger age (<5 years) and female gender. The frequency of uveitis occurred in the JIA patients in the following distribution according to subtype: oligoarthritis (43, 56%), polyarthritis RF negative (18, 23%), polyarthritis RF positive (1, 1%), psoriatic (4, 5%), ERA (1, 1%), systemic (1, 1%) and undifferentiated (9, 12%). Time (confidence intervals) from diagnosis of JIA to diagnosis of new onset uveitis image Conclusion: In a large inception cohort of newly diagnosed JIA patients followed prospectively, the overall incidence rate of new cases of uveitis was 2.9% per year. The slow decrease in incidence over time and the development of new cases of uveitis years later, highlight the importance of ongoing and long term surveillance.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.295
Teacher spread0.280 · 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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Citations4
Published2014
Admission routes2
Has abstractyes

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