Trends in Treatment, Outcomes, and Incidence of Orthopedic Surgery in Patients with Rheumatoid Arthritis: An Observational Cohort Study Using the Japanese National Database of Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: In this study, we investigated the changes in clinical outcome, treatment, and incidence of orthopedic surgery in patients with rheumatoid arthritis (RA) from 2004 to 2014. METHODS: Data were studied from the Japanese nationwide cohort database, NinJa (National Database of Rheumatic Diseases by iR-net in Japan), from 2004 to 2014. The time trends in the incidence of orthopedic procedures were analyzed using linear regression analysis. The cross-sectional annual data were compared between 2004 and 2014 to analyze the changes in clinical outcome and treatment. RESULTS: The incidence of orthopedic surgeries in patients with RA consistently decreased from 72.2 procedures per 1000 patients in 2004 to 51.5 procedures per 1000 patients in 2014 (regression coefficient = -0.0028, 95% CI -0.0038 to -0.0019, p < 0.001). The greatest reduction was found in total knee arthroplasty and total hip arthroplasty. Disease activity and functional disability improved significantly over this decade. The proportions of patients receiving methotrexate and biologic disease-modifying antirheumatic drugs significantly increased from 39.6% and 1.7% in 2004 to 63.8% and 27.4% in 2014, respectively. CONCLUSION: The overall incidence of orthopedic surgeries in patients with RA significantly decreased, accompanied by improved clinical outcomes because of the expanded use of effective drugs; however, the declining trend differed between procedures or locations. The results from the present study suggest that there might be a change in supply and demand for orthopedic surgeries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".