Rheumatoid Arthritis: Trends in Antirheumatic Drug Use, C-reactive Protein Levels, and Surgical Burden
Bibliographic record
Abstract
OBJECTIVE: Over the past decade, the therapeutic approach used to treat patients with rheumatoid arthritis (RA) has considerably changed. It remains unclear whether these changes have been accompanied by decreased disease severity and surgical treatment burden at the population level. Therefore, we investigated time trends in antirheumatic drug consumption, C-reactive protein (CRP) levels, and use of orthopedic surgery among Danish patients with RA. METHODS: Using medical databases, we identified all patients with RA living in Northern Denmark during 1996-2012. For each calendar year, we computed the annual rate of antirheumatic drug use (1996-2010), the median CRP value in mg/l (1996-2011), and the proportions of patients who underwent hip replacement and other orthopedic procedures (1996-2012). RESULTS: Antirheumatic drug consumption per patient increased 5-fold, from 145.0 defined daily doses (DDD) in 1996 to 695.4 DDD in 2010. Median CRP declined from 20.5 mg/l [interquartile range (IQR), 10.0 to 43.5 mg/l] in 1996 to 10.0 mg/l (IQR, 4.2-17.8 mg/l) in 2011. From 1996 to 2012, declining proportions of patients with RA underwent hip replacement (14.9% to 10.1%) and other joint operations (29.1% to 23.4%), while the annual proportion of patients who underwent soft tissue procedures increased from 20.7% to 23.4%. CONCLUSION: Antirheumatic drug consumption has substantially increased among patients with RA since 1996, in association with reduced disease activity (i.e., lower CRP levels), fewer joint procedures (including hip replacements), and more soft tissue procedures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".