The Longterm Effect of Early Intensive Treatment of Seniors with Rheumatoid Arthritis: A Comparison of 2 Population-based Cohort Studies on Time to Joint Replacement Surgery
Bibliographic record
Abstract
OBJECTIVE: Disease-modifying antirheumatic drugs (DMARD) have the greatest effect when initiated early. We evaluated the influence of early exposure to DMARD on time to joint replacement surgery among patients with incident rheumatoid arthritis (RA). METHOD: Using a common protocol, we undertook 2 independent population-based cohort studies of patients with incident RA aged 66 years or older in Ontario (ON) and Quebec (QC) covering the period 2000-2013. We used Cox proportional hazards regression with time-dependent variables measuring duration of drug use in the first year, separately for methotrexate (MTX) and other DMARD, adjusting for baseline demographics, clinical factors, and other potentially confounding drug exposures. Our outcome measure was any joint replacement derived from standardized procedure codes. Adjusted HR and 95% CI were estimated. RESULTS: Among 20,918 ON and 6754 QC patients with RA followed for a median of 4.5 years, 2201 and 494 patients underwent joint replacement surgery for crude event rates of 2.0 and 1.4 per 100 person-years, respectively. Greater cumulative exposure to MTX (HR 0.97, 95% CI 0.95-0.98) and other DMARD (HR 0.98, 95% CI 0.97-0.99) in the first year after diagnosis was associated with longer times to joint replacement in ON, corresponding to a 2-3% decrease in the hazard of surgery with each additional month of early use. Similar results were observed in QC. CONCLUSION: Greater duration of exposure to DMARD soon after RA diagnosis was associated with delays to joint replacement surgery in both provinces. Early intensive treatment of RA may ultimately reduce demand for joint replacement surgery.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".