Differences in Cause-Specific Mortality in Patients With Rheumatoid Arthritis by Sex and Seropositivity
Bibliographic record
Abstract
Objective Our aims were to investigate the trends in observed vs expected all-cause mortality and the effects of serostatus and sex on all-cause and cause-specific mortality in individuals with rheumatoid arthritis (RA) in a large community-based longitudinal cohort study. Methods This retrospective cohort study evaluated all-cause and cause-specific mortality in RA using a community population–based inception cohort of adult residents of Olmsted County, Minnesota, with incident RA from 1980 to 2019 meeting American College of Rheumatology 1987 criteria, and non-RA residents matched 3:1 to patients with RA by age and sex. Underlying cause of death was obtained from death certificates. Competing risks/cumulative incidence methods were used to compare cause-specific mortality incidence between individuals with and without RA. Cox models estimated the effect of RA on risk of cause-specific mortality, adjusting for sex, age, and calendar year of index. Interactions for RA with sex, age, seropositivity, and calendar year were examined. Results The study included 1337 patients with RA and 4011 non-RA comparators. All-cause mortality was significantly increased in both female and male individuals with RA, specifically in seropositive RA, and declined after the 1980s. The increased risk of death due to respiratory causes in patients with RA was significantly different depending on seropositive vs seronegative status (adjusted hazard ratio 2.26 [95% CI 1.56-3.26] vs 0.52 [95% CI 0.24-1.13], interaction P < 0.001). There were no significant differences in cause-specific mortality based on sex. Conclusion Seropositivity in persons with RA associates with a significantly increased risk of death, particularly due to respiratory causes, whereas no increased risk was found in seronegative patients.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".