Trends in Excess Mortality Among Patients With Rheumatoid Arthritis in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To evaluate excess mortality over time, comparing rheumatoid arthritis (RA) patients with the general population. METHODS: We computed all-cause mortality rates among Ontario residents age ≥15 years with RA versus without RA from 1996 to 2009. Age- and sex-standardized mortality rates were expressed as the number of deaths per 1,000 population. Excess mortality rates were calculated as the difference between death rates among RA patients and those in the general population. We estimated standardized mortality ratios (SMRs) and mortality rate ratios (MRRs) to assess relative excess mortality over time. RESULTS: From 1996 to 2009, SMRs in RA ranged from 13.0 (95% confidence interval [95% CI] 12.2, 13.9) to 9.2 deaths per 1,000 RA patients (95% CI 8.4, 10.0); and for those without RA from 8.7 (95% CI 8.6, 8.7) to 6.0 deaths (95% CI 5.9, 6.0) per 1,000 general population. Over the study period, the excess mortality rate among RA patients was approximately 3 excess deaths per 1,000 population. Relative reductions in standardized mortality rates occurred over time for those with and without RA (-21.4% versus -13.4%). The SMRs for RA patients in 1996-1997, 2000-2001, 2004-2005, and 2008-2009 were 1.51 (95% CI 1.43, 1.59), 1.50 (95% CI 1.43, 1.57), 1.43 (95% CI 1.37, 1.50), and 1.41 (95% CI 1.35, 1.47), respectively. We did not find a significant change in the MRR by calendar time. CONCLUSION: Mortality for RA patients has decreased over time but remains elevated compared to the general population, with 40-50% more deaths among RA patients. The relative excess mortality over time (mortality gap) remains unchanged in our sample.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".