Ten-year risk of cerebrovascular accidents in incident rheumatoid arthritis: a population-based study of trends over time
Bibliographic record
Abstract
OBJECTIVE: To evaluate secular trends in 10-year risk of incident cerebrovascular accidents (CVA), in incident RA relative to the general population. METHODS: We conducted a retrospective study of a population-based incident cohort with RA onset from 1997 to 2004 in British Columbia, Canada, with matched general population controls (2:1), using administrative health data. RA and general population cohorts were divided according to year of RA onset, defined according to the first RA visit of the case definition. Incident CVA was defined as the first CVA occurring within 10 years from the first RA visit. Secular trend was assessed using delayed-entry Cox models with a two-way interaction term between the year of RA onset and indicator of RA vs general population. Linear, quadratic and spline functions of year of RA onset were compared with assess non-linear effects. The model with the lowest Akaike Information Criterion was selected. RESULTS: Overall, 23 545 RA and 47 090 general population experienced 658 and 1220 incident CVAs, respectively. A spline Cox model with a knot at year of onset 1999 was selected. A significant decline in risk of CVA was observed in individuals with RA onset after 1999 [0.90 (0.86, 0.95); P = 0.0001]. The change in CVA risk over time differed significantly in RA with onset from 1999 onwards compared with the general population (P-value of interaction term = 0.03), but not before 1999 (P = 0.06). CONCLUSION: Our findings suggest that people with RA onset from 1999 onwards, had a significantly greater decline in 10-year risk of CVA compared with the general population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".