Failure in Longterm Treatment is Rare in Actively Treated Patients with Rheumatoid Arthritis, But May Be Predicted by High Health Assessment Score at Baseline and by Residual Disease Activity at 3 and 6 Months: The 5-year Followup Results of the Randomized Clinical NEO-RACo Trial
Bibliographic record
Abstract
OBJECTIVE: With modern initial aggressive combination treatments with synthetic disease-modifying antirheumatic drugs (sDMARD), most patients with rheumatoid arthritis (RA) achieve remission, have marginal radiographic progression, and sustain normal function. Here we aim to identify the patients failing these targets even after aggressive treatment. METHODS: Ninety-nine patients with early, active RA were treated with a combination of 3 sDMARD and prednisolone (PRD), and either infliximab or placebo infusions during the first 6 months, aiming at strict remission. After 24 months, the treatments became unrestricted. At 60 months, 4 evident clinical features of treatment failure were defined: area under curve (AUC) between 6-60 months for disease activity score assessing 28 joints > 2.6; AUC 6-60 for health assessment questionnaire > 0.5; progression in total Sharp/van der Heijde score 0-60 months > 3 units; and need of PRD or biologic DMARD treatment at 60 months. RESULTS: A total of 93 patients were followed up for 60 months. Of them, 45 had no features of treatment failure, 30 had 1, 10 had 2, 7 had 3, and 1 patient had all 4 features. Having 2-4 features of treatment failure at 5 years was predicted by the health assessment score at baseline, and by even low residual disease activity at 3 and 6 months. CONCLUSIONS: Only 20% of the patients with RA treated early with combination sDMARD and PRD have more than 1 clinical feature of treatment failure at 60 months. Residual clinical disease activity at 3-6 months was the most important predictor for identifying these patients. The study was registered at www.clintrials.gov (NCT00908089).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".