Association of Arthritis Onset with Influenza: Analysis of the Canadian Early Inflammatory Arthritis Cohort
Bibliographic record
Abstract
Objective To evaluate seasonal patterns of early inflammatory arthritis (IA) onset and potential associations with IA symptom onset. Methods The Canadian Early Arthritis Cohort (CATCH) is an inception cohort study of adults with early (12 months or less) IA. We used patient reports of symptom onset as a proxy of IA onset and examined the seasonal distribution of IA onset over 10 years. Influenza time series was based on laboratory‐confirmed influenza A and B from the Canadian FluWatch surveillance from 2010‐2016. Bivariate analysis of influenza and IA was performed using cross‐correlations with different time lags and Poisson regression. IA and influenza were recorded as monthly total frequencies. Results Of 2519 IA patients, 88% had confirmed rheumatoid arthritis (RA). Significantly, more IA onsets occurred in winter compared with other seasons (P = 0.03); although IA onset was more frequent in January, the difference between months was not statistically significant. Compared to months with the lowest influenza rates, months with the highest influenza rates had a statistically significant, but trivial, increase of 0.003% in the incidence of IA (incidence rate ratio (95% confidence interval): 1.00003 (1.00005; 1.000053), P = 0.02). Conclusion Although IA symptom onset occurs more frequently in winter, we found that flu outbreaks were not associated with a meaningful increase in IA symptom onset in a large, well‐characterized cohort of Canadian adults over 6 years.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".