Use of Disease-modifying Antirheumatic Drugs, Biologics, and Corticosteroids in Older Patients With Rheumatoid Arthritis Over 20 Years
Bibliographic record
Abstract
OBJECTIVE: To examine changes in prescribing patterns, especially the use of corticosteroids (CS), in patients with rheumatoid arthritis (RA) over 2 decades. METHODS: This was a secondary analysis of health administrative data using a previously validated dataset and case definition for RA. Cases were matched 1:4 by age and sex to controls within a population of approximately 1 million inhabitants with access to universal health care. Longitudinal data for incident and prevalent RA cases were studied between 1997 and 2017. RESULTS: There were 8240 RA cases (all ≥ 65 yrs) with a mean (SD) age 72.2 (7.5) years and 70.6% were female. Over 20 years, annual utilization of coxibs in prevalent RA cases fell with a concomitant increase in disease-modifying antirheumatic drugs (DMARDs) and biologics. Over the same period, CS use was largely unchanged. Approximately one-third of patients had at least 1 annual prescription for CS, most frequently prednisone. The mean annual dose showed a modest reduction and the duration of utilization in each year shortened. Rheumatologists prescribed CS less frequently and in lower doses than other physician groups. For incident RA cases, there was a significant fall in annual prescribed dose of prednisone by rheumatologists over time. CONCLUSION: In older adults with RA, the utilization of DMARDs and biologics has increased over the past 20 years. However, the use of CS has persisted. Renewed efforts are required to minimize their use in the long-term pharmacological management of RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".