Patient factors associated with waiting time to pediatric rheumatologist consultation for patients with juvenile idiopathic arthritis
Bibliographic record
Abstract
BACKGROUND: Early diagnosis and treatment of Juvenile Idiopathic Arthritis (JIA) is essential to optimize outcomes. Wait times (WTs) to consultation with a pediatric rheumatologist consultation is a Canadian quality measure, with benchmarks set at 7 days for systemic JIA (sJIA) and 4 weeks for other JIA categories. In this study we assess WTs for JIA at a single academic center and describe factors associated with longer WTs. METHODS: This was a retrospective cohort study of 164 patients enrolled in a pharmacogenetic study in Alberta between 2002 and 2018. Limited chart reviews were conducted to evaluate dates of referral and first rheumatology visit to calculate WTs for receipt of pediatric rheumatology care. Cox proportional hazard models identified factors associated with WTs considering variables at the first pediatric rheumatology visit including: JIA category, age, sex, distance to the pediatric rheumatology clinic, number of active joints, pain and C-reactive protein. RESULTS: The median age at diagnosis was 8.0 years (interquartile range, IQR 3.5, 12.0) and 46% of patients had oligoarticular JIA. Only 18 patients (11%) were from rural locations. The median WT for all patients met the national benchmark (22 days, IQR, 9, 44) with no statistically significant difference between WTs among JIA categories (p = 0.055). Importantly, the majority of sJIA cases met the 7-day benchmark (67%) with a median WT of 1.5 days. Older age was associated with longer WT (HR 0.94, 95% CI 0.89, 0.98, p = 0.005). CONCLUSION: Median benchmarks were met, however delays in older patients highlight the need for monitoring WTs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".