Remission in Early Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: We systematically reviewed remission as an outcome measure in observational studies and randomized controlled trials (RCT) in early rheumatoid arthritis (RA). Our objectives were to identify its frequency using different criteria, to determine the influence of different treatment strategies on remission, and to review the effects of remission on radiological outcomes. METHODS: Pubmed, Medline and Embase were searched using the following terms: Early Rheumatoid Arthritis or Early RA combined with Remission, Treatment, anti-Tumor Necrosis Factor (TNF) or Disease-modifying Antirheumatic Drugs (DMARD). Remissions were reported using American College of Rheumatology (ACR) criteria and Disease Activity Score (DAS) criteria. RESULTS: Seventeen observational studies (4762 patients) reported remission in 27% of patients, 17% by ACR criteria and 33% by DAS criteria. Twenty RCT (4 comparing DMARD monotherapies, 13 comparing monotherapy with combination therapies, 3 comparing combination therapies) enrolled 4290 patients. ACR remissions occurred in 16% receiving DMARD monotherapy and 24% combination therapies (random effects OR 1.69, 95% CI 1.12-2.36). DAS remissions occurred in 26% and 42%, respectively (OR 2.01, 95% CI 1.46-2.78). Observational studies showed continuing radiological progression despite remission. RCT showed less radiological progression in remission when treated with combination therapy compared to monotherapies. CONCLUSION: Remission is a realistic treatment goal in early RA. Combination therapies using DMARD with or without TNF inhibitors increase remissions. Radiological progression occurred in remission but is reduced by combination therapies. ACR and DAS remission criteria are not directly comparable and standardization is needed.
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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 | 0.036 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".