Rheumatoid Arthritis Therapy and Joint-replacement Surgery: Are We Making a Difference?
Bibliographic record
Abstract
Outcomes for patients with rheumatoid arthritis (RA) have improved over the past decade, owing to recognition of the importance of a structured “treat-to-target” treatment protocol and of early treatment, in addition to increasing access to potent therapies. American College of Rheumatology response criteria, quality of life measures, and differences in radiographic progression have all been used as treatment endpoints to demonstrate improvement. Studies such as Grigor et al ’s TICORA and Verstappen et al ’s CAMERA clearly demonstrate that disease management protocols emphasizing tight control result in lower disease activity, more patients in remission, and less joint damage1,2. Initiation of therapy and achieving low disease activity early in the first year of disease has a major effect on clinical and radiographic outcomes years later3. Finally, initiating aggressive treatment after onset of RA and maintaining tight control is a feasible “real world” strategy4. These changes provide the backdrop for reports that confirm a clear decrease in orthopedic surgery of the upper extremity and soft tissue for patients with rheumatoid arthritis (RA), although rates appear stable for total knee (TKA) and total hip arthroplasty (THA)5,6. In this context, there is now greater interest in using rates of orthopedic surgery, and more specifically, arthroplasty as endpoints to indicate successful disease control in patients with RA. In this issue of The Journal Widdifield and colleagues describe 2 cohorts of incident RA patients over 66 years of age in Ontario and Quebec, and investigate the relationship between the use of methotrexate (MTX) and other disease-modifying antirheumatic drug (DMARD) therapy and the rate of arthroplasty7. The outcome of interest was … Address correspondence to Dr. S.M. Goodman, Hospital for Special Surgery, 535 East 70th St., New York, New York 10021, USA. E-mail: goodmans{at}hss.edu
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".