Infection Rates Before and After Diagnosis of IgA Vasculitis in Childhood: A Population-wide Study Using Non-exposed Matched Controls
Bibliographic record
Abstract
OBJECTIVE: Clinical data suggest that infections can trigger IgA vasculitis (IgAV), but longterm observations are lacking. We compared rates, types, and microorganisms for serious infection before and after diagnosis for children with IgAV and non-exposed controls. METHODS: Using population-based administrative linked health datasets we estimated incidence rates (IR) for serious infection per 1000 person-months for patients with IgAV (n = 504, age 5 yrs, 59.1% males) and controls matched for age, sex, and year of presentation (n = 1281, age 6 yrs, 66% males). Time zero (T0) was the date of IgAV diagnosis or equivalent date in controls, lookback (median 38 mos) was the period prior to T0, and followup (median 239 mos) was the period after T0. RESULTS: During lookback, prevalence of serious infection was similar in patients with IgAV and controls (11.5% vs 9.5%, respectively), but patients with IgAV had a higher rate of upper respiratory tract infections [incidence rate ratio (IRR) 1.79; 95% CI 1.39-2.31] with shorter time between first serious infection and T0 (27 vs 43 mos; p = 0.02). During followup, patients were at a constant increased risk for serious infections (IRR 1.46, 95% CI 1.35-1.58). These rates were higher during followup: sepsis (IRR 12.6), pneumonia (IRR 6.19), upper respiratory tract infections (IRR 2.36), and skin infections (IRR 1.85). There was little overlap between patients with serious infections in the lookback and followup periods. CONCLUSION: In patients with childhood IgAV there is an increased longterm risk for a broader spectrum of infections, which is unrelated to serious infections prior to diagnosis or treatment. This suggests disease-specific factors may have a lasting effect on immune competence in childhood IgAV.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".