Multimorbidity Burden in Rheumatoid Arthritis: A Population-based Cohort Study
Bibliographic record
Abstract
Objective To estimate the prevalence and incidence of multimorbidity (MM) in a population-based cohort of patients with rheumatoid arthritis (RA) compared to subjects without RA. Methods Between 1999–2013, residents of Olmsted County, Minnesota with incident RA who met the 1987 American College of Rheumatology criteria were compared to age- and sex-matched non-RA subjects from the same population. Twenty-five chronic comorbidities from a combination of the Charlson, Elixhauser, and Rheumatic Disease Comorbidity Indices were included, excluding rheumatic comorbidities. The Aalen-Johansen method was used to estimate the cumulative incidence of MM (MM2+; ≥ 2 chronic comorbidities) or substantial MM (MM5+; ≥ 5), adjusting for the competing risk of death. Results The study included 597 patients with RA and 594 non-RA subjects (70% female, 90% White, mean age 55.5 yrs). At incidence/index date, the prevalence of MM2+ was higher in RA than non-RA subjects (38% RA vs 32% non-RA, P = 0.02), whereas prevalence of MM5+ was similar (5% RA vs. 4% non-RA, P = 0.68). During follow-up (median 11.6 yrs RA, 11.3 yrs non-RA), more patients with RA developed MM2+ (214 RA vs 188 non-RA; adjusted HR 1.39, 95% CI 1.14–1.69). By 10 years after RA incidence/index, the cumulative incidence of MM2+ was 56.5% among the patients with RA (95% CI 56.5–62.3%) compared with 47.9% among the non-RA (95% CI 42.8–53.7%). Patients with RA showed no evidence of increase in incidence of MM5+ (adjusted HR 1.17, 95% CI 0.93–1.47). Conclusion Patients with RA have both a higher prevalence of MM at the time of RA incidence as well as increased incidence thereafter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".