Comorbidities Before and After the Diagnosis of Rheumatoid Arthritis: A Matched Longitudinal Study
Bibliographic record
Abstract
OBJECTIVE: To determine the contribution of rheumatoid arthritis (RA) to conditions and medical events. A secondary objective is to quantify this association before and after the introduction of biologic medications. METHODS: All data were collected as health administrative data in Ontario, Canada. Patients with RA (n = 136 678) matched 1:1 to a pool of possible controls without RA from 1995 to 2016. The study was a retrospective longitudinal observational administrative data-based cohort study with cases (RA) and controls (two non-RA comparator groups). The main exposure was new-onset RA identified by a validated diagnosis algorithm. The secondary exposure was the calendar year, which provided a natural experiment to compare years in which biologics were unavailable (pre-2001) to increasing utilization over time. The main outcomes were counts of 27 Johns Hopkins Expanded Diagnostic Cluster Comorbid Conditions. Outcomes were reported as counts and percentage differences between cases and matched controls. RESULTS: Patients experienced increases in conditions and medical events up to 5 years before RA disease incidence-4.9 conditions per patient-year compared with 4.6 conditions per patient-year in matched controls. Comorbidities increased to 8.7 conditions per patient-year in the year of RA incidence but were lower in the years after diagnosis-6.9 conditions per patient-year at 5 years postdiagnosis. CONCLUSION: This study reframes the clinical manifestations of RA with detailed data on the marginal contribution of RA to conditions and medical events. These results show that a large portion of disease burden is due to the indirect effects of RA.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".