Clinical and Serological Predictors of Remission in Rheumatoid Arthritis Are Dependent on Treatment Regimen
Bibliographic record
Abstract
OBJECTIVE: Early intensive treatment is now the cornerstone for the management of rheumatoid arthritis (RA). In the era of personalized medicine, when treatment is becoming more individualized, it is unclear from the current literature whether all patients with RA benefit equally from such intensive therapies. We investigated the benefit of different treatment regimens on remission rates when stratified to clinical and serological factors. METHODS: The Combination Anti-rheumatic Drugs in Early Rheumatoid Arthritis (CARDERA) trial recruited patients with RA of less than 2 years' duration who had active disease. The trial compared 4 treatment regimens: methotrexate monotherapy, 2 different double therapy regimens (methotrexate and cyclosporine or methotrexate and prednisolone) and 3-drug therapy. Clinical predictors included age, male sex, and tender joint count (TJC) and serological biomarkers included rheumatoid factor (RF) and anticitrullinated protein antibodies (ACPA). RESULTS: Patients who were male, over 50 years, had ≥ 6 TJC, were RF-IgM-positive, or ACPA-positive were more likely to achieve remission at 24 months using 3-drug therapy compared to monotherapy (OR 2.99, 4.95, 2.71, 2.54, and 3.52, respectively). There were no differences in response to monotherapy and 3-drug therapy if patients were female, under 50 years, had < 6 TJC, or were seronegative. CONCLUSION: Early intensive regimens have become the gold standard in the treatment of early RA. Our study suggests that this intensive approach is only superior to monotherapy in certain subsets of patients. Although these are unlikely to be the only predictors of treatment response, our study brings us a step closer to achieving personalized medicine in RA.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".