Mortality rates in children, young people, and young adults with JIA: an observational study using the Clinical Practice Research Datalink (CPRD)
Bibliographic record
Abstract
Objectives: Many rheumatological conditions have increased mortality rates; however, there is conflicting evidence regarding the impact of juvenile idiopathic arthritis (JIA) on mortality. This analysis aimed to calculate the all-cause mortality rate of patients with JIA, compared with (a) matched-control patients and (b) the general population mortality estimates. Methods: Using the UK General Practice data Clinical Practice Research Datalink, JIA cases starting <16 years old from England were included, matched 4-to-1 with non-JIA controls (birth year, gender, practice). Exposure started on first JIA-code date (matched-date for controls) or January 1, 2000, whichever was latest. Follow-up continued until December 31, 2018, or death, whichever was first. Cox-proportional hazards models compared mortality in JIA versus matched controls. JIA rates were stratified by systemic versus non-systemic JIA. Standardised mortality rates (SMRs) were generated for JIA versus general population estimates (calendar year, age, gender). Results: There were 4762 patients with JIA (30 deaths) and 13 957 matched controls (27 deaths); patient demographics were similar between cohorts. Mortality rate for JIA was 6.2/10 000 person years (95% CI: 4.3-8.9), and 1.9/10 000 person years (95% CI: 1.3-2.8) for matched controls; JIA patients had 3.3 times higher mortality (95% CI: 2.0-5.5). Patients with systemic JIA had 3.3 times higher mortality (95% CI: 1.6-6.9) versus those with non-systemic JIA. The SMR for JIA was 2.9 (95% CI: 2.1-4.2). Eighteen (60%) JIA deaths occurred before 2012. Conclusions: This analysis calculated mortality rates in young people with JIA in England. Death in young people with JIA is exceedingly rare. Higher rates were observed in patients with systemic JIA. Over half the deaths occurred prior to 2012 when biologic treatment, particularly for systemic JIA, was limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".