Use of Disease-modifying Antirheumatic Drugs for Inflammatory Arthritis in US Veterans: Effect of Specialty Care and Geographic Distance
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of access to and distance from rheumatology care on the use of disease-modifying antirheumatic drugs (DMARD) in US veterans with inflammatory arthritis (IA). METHODS: Provider encounters and DMARD dispensations for IA (rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis) were evaluated in national Veterans Affairs (VA) datasets between January 1, 2015, and December 31, 2015. RESULTS: Among 12,589 veterans with IA, 23.5% saw a rheumatology provider. In the general IA population, 25.3% and 13.6% of veterans were exposed to a synthetic DMARD (sDMARD) and biologic DMARD (bDMARD), respectively. DMARD exposure was 2.6- to 3.4-fold higher in the subpopulation using rheumatology providers, compared to the general IA population. The distance between veterans' homes and the closest VA rheumatology site was < 40 miles (Near) for 55.9%, 40-99 miles (Intermediate) for 31.7%, and ≥ 100 miles (Far) for 12.4%. Veterans in the Intermediate and Far groups were less likely to see a rheumatology provider than veterans in the Near group (RR = 0.72 and RR = 0.49, respectively). Exposure to bDMARD was 34% less frequent in the Far group than the Near group. In the subpopulation who used rheumatology care, the bDMARD exposure discrepancy did not persist between distance groups. CONCLUSION: Use of rheumatology care and DMARD was low for veterans with IA. DMARD exposure was strongly associated with rheumatology care use. Veterans in the general IA population living far from rheumatology sites accessed rheumatology care and bDMARD less frequently than veterans living close to rheumatology sites.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".