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Record W6966611977 · doi:10.3899/jrheum.2025-0314.136

Evaluating Access to Care Indicators of JIA Patients in Southwestern Ontario

2025· article· en· W6966611977 on OpenAlexaffvenueabout

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsUniversity of British ColumbiaInstitute for Clinical Evaluative SciencesUniversity of TorontoSickKids FoundationLondon Health Sciences CentreMcMaster University
Fundersnot available
KeywordsReferralRheumatologyPercentileMedical recordHealth careRetrospective cohort studyArthritisDescriptive statistics

Abstract

fetched live from OpenAlex

Objectives Delays in initial consultation for patients with newly diagnosed juvenile idiopathic arthritis (JIA) are associated with worse outcomes.[1] Canadian benchmarks exist for acceptable wait times for initial consultation in JIA; non-systemic JIA patients should be seen within 4 weeks.[2] We sought to identify gaps in access to care in Ontario to plan for a province-wide quality improvement project. This study aimed to establish baseline measures for access to care indicators for patients with newly diagnosed JIA at pediatric rheumatology centers in Southwestern Ontario. Methods A retrospective chart review was completed for patients with non-systemic JIA assessed by Pediatric Rheumatology for consultation between January 1-June 30, 2024, at McMaster Children’s Hospital or Children’s Hospital, London Health Sciences Centre. Descriptive statistics were used to analyze wait times, patients meeting benchmarks, and general access to care measures. Results Twenty-one patients with newly diagnosed JIA were assessed during the first half of 2024. The median (IQR) wait time from referral to consultation was 16 days (IQR=9-30) and the 90th percentile wait time was 50 days. The wait time benchmark was achieved in 71.4% of patients with newly diagnosed JIA. There was regional variation in meeting the wait time benchmark between Hamilton (53.3%) and London (88.9%). No patients missed or rescheduled their initial consultation visit. Patients with newly diagnosed JIA saw a median of 2 medical providers prior to pediatric rheumatology. The average distance traveled to the pediatric rheumatology center was 55.6 km. The percentage of patients meeting the wait time benchmark were similar for those living within 25 km (85.7%), between 25-75 km (60%), and >75 km (75%). Conclusion Overall, the wait times for pediatric rheumatology consultation are reasonable for patients with newly diagnosed JIA; however, almost 30% of patients were not seen within the recommended benchmark. With the existing triage processes in Southwestern Ontario, patient distance from the care center did not seem to affect timely access to care. Regional variation exists between Pediatric Rheumatology sites and process mapping the triage workflow could help better understand factors contributing to these regional differences. Our next step will be the inclusion of the additional Pediatric Rheumatology academic centers in Ontario. A potential candidate for a province-wide quality improvement project is increasing the percentage of new JIA patients, currently 71.43%, being seen within the recommended wait time benchmark. [1.] McErlane F. Rheumatology (Oxford) 2016;55:1225-34. [2.] Barber C. J Rheumatolgoy 2016;43:530-40.

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)0.004
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 score0.036
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.032
GPT teacher head0.371
Teacher spread0.340 · 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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Citations0
Published2025
Admission routes3
Has abstractyes

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