Time to Disease-modifying Antirheumatic Drug Treatment in Rheumatoid Arthritis and Its Predictors: A National, Multicenter, Retrospective Cohort
Bibliographic record
Abstract
OBJECTIVE: To determine the proportion of patients with rheumatoid arthritis (RA) under rheumatologic care treated with disease-modifying antirheumatic drugs (DMARD) within 6 months from symptom onset and the components of time to treatment and its predictors. METHODS: A historical inception cohort of 339 patients with RA randomly selected from 18 rheumatology practices was audited. The proportion that initiated DMARD treatment within 6 months from symptom onset was estimated using Kaplan-Meier analysis. Time to each component of the care pathway was estimated. Multivariable modeling was used to determine predictors of early treatment using 12 preselected variables available in the clinical charts. Bootstrapping was used to validate the model. RESULTS: Within 6 months from symptom onset, 41% (95% CI 36%-46%) of patients were treated with DMARD. The median time to treatment was 8.4 (interquartile range 3.8-24) months. Events preceding rheumatology referral accounted for 78.1% of the time to treatment. The most prominent predictor of increased time to treatment was a concomitant musculoskeletal condition, such as osteoarthritis or fibromyalgia. The significance of other variables was less consistent across the models investigated. Included variables accounted for 0.69 ± 0.03 of the variability in the model. CONCLUSION: Fewer than 50% of patients with RA are treated with DMARD within 6 months from symptom onset. Time to referral to rheumatology represents the greatest component delay to treatment. Concomitant musculoskeletal condition was the most prominent predictor of delayed initiation of DMARD. Implications of these and other findings warrant further investigation.
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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 | 0.000 | 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".