Patterns of Disease‐Modifying Antirheumatic Drug Use in Rheumatoid Arthritis Patients After 2002: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To report and synthesize patterns of disease-modifying antirheumatic drug (DMARD) use reported in observational studies of patients with established and early rheumatoid arthritis (RA) after publication of the American College of Rheumatology guidelines promoting universal DMARD use. METHODS: We searched PubMed for full-length articles in English published between January 1, 2002 and October 1, 2012 that examined DMARD use. The data abstracted from articles included the patient characteristics, country of study, time period studied, patient source, and treating physician type. Study quality was assessed using a modified Newcastle-Ottawa Quality Assessment Scale. RESULTS: We reviewed 1,287 abstracts; 98 full-length articles were selected for additional review and 27 studies describing 28 cohorts of patients were included. Twelve studies described data from cohorts of patients with established RA, and DMARD use in this group of studies ranged from 73-100%. Five studies described data from patients sourced through administrative data and demonstrated consistently lower DMARD use, ranging from 30-63%. Three studies conducted population-based surveys to define cases of RA where DMARD use ranged from 47-73%. Eight studies investigated patients with early RA. DMARD use among patients followed by rheumatologists ranged from 77-98%, whereas DMARD use reported for patients seen by a mix of physicians was significantly lower (39-63%). CONCLUSION: DMARD use in studies from RA cohorts or registries (in which patients were followed by rheumatologists) ranged from 73-100%, compared with 30-73% in studies from administrative data or population-based surveys (in which patients were not necessarily receiving rheumatology subspecialty care).
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".