Systematic Monitoring of Disease Activity Using an Outcome Measure Improves Outcomes in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To systematically review the literature on the value of outcome measures to monitor treatment response in patients with rheumatoid arthritis (RA). METHODS: Canadian rheumatologists participating in the International 3e (evidence expertise exchange) Initiative formulated the question "Which parameters should be recommended for use in the management of RA patients to assess a clinically meaningful response in clinical practice?". Searches in 3 electronic databases, Medline, Embase, and Cochrane Central Register of Controlled Trials, yielded no relevant study addressing this question. Experts in the field proposed to extrapolate evidence from 3 randomized controlled trials of systematic monitoring or tight control strategy in the management of RA. RESULTS: Three studies were included in this review. The TICORA study showed that intensive management using systematic monitoring with the Disease Activity Score (DAS) aiming at least low disease activity, monthly followup, and more aggressive disease-modifying antirheumatic drug (DMARD) treatment improves outcomes with higher remission rates (65% vs 16%; p < 0.0001). Fransen, et al demonstrated that targeted therapy aimed at low disease activity (DAS28 < 3.2) led to more changes in DMARD treatment, resulting in a larger number of patients with low disease activity (31% vs 16%; p = 0.028). The CAMERA study showed that systematic monitoring using the objective computer decision program evaluation and monthly followup yielded a greater remission rate (50% vs 37%; p = 0.0001). CONCLUSION: Systematic monitoring of disease activity, aiming for at least low disease activity, and frequent followup improves outcome 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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".