Biologic and Glucocorticoid Use after Methotrexate Initiation in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Biologic therapies can improve disease control for patients with rheumatoid arthritis (RA) but may be both overused and underused. We aimed to identify predictors of greater use of biologic therapies and to identify factors associated with persistent glucocorticoid use. METHODS: Using national US Veteran's Affairs databases 2005-2016, we identified patients with RA receiving a first-ever prescription of methotrexate (MTX), requiring ≥ 6 months of baseline data. We evaluated predictors of biologic therapy initiation within 2 years of starting MTX and factors associated with baseline and persistent glucocorticoid use at 6-12 months using multivariable models. RESULTS: Among 17,415 patients starting MTX, 3263 patients received biologic therapy within 2 years (20.6% 2-yr incidence). In adjusted analyses, biologic use was substantially lower in older patients [e.g., aHR 0.20 (95% CI 0.16, 0.26) for patients ≥ 80 vs < 50] and patients with more comorbidities [aHR 0.79 (95% CI 0.72, 0.87) for Charlson score ≥ 3 vs < 3]. Patients with heart failure [aHR 0.68 (95% CI 0.54, 0.84)], cancer [aHR 0.78 (95% CI 0.66, 0.92)], or who were nonwhite [aHR 0.79 (95% CI 0.72, 0.87)] were also less likely to receive a biologic. In contrast, baseline and persistent glucocorticoid use was similar across age groups and more common in patients with greater comorbidity. CONCLUSION: Biologic therapy is initiated less frequently in patients with RA who are older, have more comorbidities, and who are nonwhite. While biologics may be avoided in older and sicker patients because of safety concerns, glucocorticoid use is similar regardless of age and is more frequent in patients with comorbidities, with implications for patient outcomes.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".