Flare Rate in Patients with Rheumatoid Arthritis in Low Disease Activity or Remission When Tapering or Stopping Synthetic or Biologic DMARD: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To evaluate the risk of having a disease flare in patients with rheumatoid arthritis (RA) with low disease activity (LDA) or in remission when deescalating (tapering or stopping) disease-modifying antirheumatic drug (DMARD) therapy. METHODS: A search in medical databases including publications from January 1950 to February 2015 was performed. Included were trials and observational studies in adults with RA who were in LDA or remission, evaluating ≥ 20 patients tapering or stopping DMARD. Flare rates had to have been reported. A metaanalysis was performed on studies deescalating tumor necrosis factor (TNF) blockers. RESULTS: Four studies evaluated synthetic DMARD. Flare rates ranged from 8% at 24 weeks to 63% at 4 months after deescalation. Fifteen studies reported on TNF blockers. Estimated flare rates by metaanalysis on studies tapering or stopping TNF blockers were 0.26 (95% CI 0.17-0.39) and 0.49 (95% CI 0.27-0.73) for good-quality and moderate-quality studies, respectively. Flare rates in 3 studies stopping tocilizumab were 41% after 6 months, 55% at 1 year, and 87% at 1 year. Flare rates in 3 studies deescalating abatacept were 34% at 1 year, 41% at 1 year, and 72% at 6 months. Five studies evaluating radiographic progression in patients deescalating treatment all found limited to no progression. CONCLUSION: Results suggest that more than one-third of patients with RA with LDA or in remission may taper or stop DMARD treatment without experiencing a disease flare within the first year. Dose reduction of TNF blockers results in lower flare rates than stopping and may be noninferior to continuing full dose. Radiological progression after treatment deescalation remains low, but may increase slightly.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".