Comorbid conditions as risk factors for West Nile neuroinvasive disease in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
West Nile neuroinvasive disease (WNND) is a severe neurological illness that can result from West Nile virus (WNV) infection, with long-term disability and death being common outcomes. Although WNV arrived in North America over two decades ago, risk factors for WNND are still being explored. The objective of this study was to identify WNND comorbid risk factors in the Ontario population using a retrospective, population-based cohort design. Incident WNV infections from laboratory records between 1 January 2002 - 31 December 2012 were individually-linked to health administrative databases to ascertain WNND outcomes and comorbid risk factors. WNND incidence was compared among individuals with and without comorbidities using risk ratios (RR) calculated with log binomial regression.Three hundred and forty-five individuals developed WNND (18.3%) out of 1884 WNV infections. West Nile encephalitis was driving most associations with comorbidities. Immunocompromised (aRR 2.61 [95% CI 1.23-4.53]) and male sex (aRR 1.32 [95% CI 1.00-1.76]) were risk factors for encephalitis, in addition to age, for which each 1-year increase was associated with a 2% (aRR 1.02 [95% CI 1.02-1.03]) relative increase in risk. Our results suggest that individuals living with comorbidities are at higher risk for WNND, in particular encephalitis, following WNV infection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".