Incidence of chronic immune-mediated inflammatory diseases after diagnosis with Kawasaki disease: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: Kawasaki disease (KD) is an immune-mediated vasculitis of childhood with multi-organ inflammation. We determined the risk of subsequent immune-mediated inflammatory disease (IMID), including arthritis, type 1 diabetes, IBD, autoimmune liver disease, primary sclerosing cholangitis and multiple sclerosis. METHODS: We conducted a matched population-based cohort study using health administrative data from Ontario, Canada. Children aged <18 years born between 1991 and 2016 diagnosed with KD (n = 3753) were matched to 5 non-KD controls from the general population (n = 18 749). We determined the incidence of IMIDs after resolution of KD. Three- and 12-month washout periods were used to exclude KD-related symptoms. RESULTS: There was an elevated risk of arthritis in KD patients compared with non-KD controls, starting 3 months after index date [103.0 vs 12.7 per 100 000 person-years (PYs); incidence rate ratio 8.07 (95% CI 4.95, 13.2); hazard ratio 8.08 (95% CI 4.95, 13.2), resulting in the overall incidence of IMIDs being elevated in KD patients (175.1 vs 68.0 per 100 000 PYs; incidence rate ratio 2.58 (95% CI 1.93, 3.43); hazard ratio 2.58, 95% CI 1.94, 3.43]. However, there was no increased risk for diabetes, IBD, autoimmune liver disease, primary sclerosing cholangitis or multiple sclerosis in KD patients. Similar results were observed using a 12-month washout period. CONCLUSION: Children diagnosed with KD were at increased risk of arthritis following the acute KD event, but not other IMIDs. Health-care providers should monitor for arthritis in children following a diagnosis of KD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".