Clinical trials of new drugs for the treatment of rheumatoid arthritis: focus on early disease
Bibliographic record
Abstract
The European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases convened a task force of experts in rheumatoid arthritis (RA) and clinical trial methodology to comment on the new draft 'Guideline on clinical investigation of medicinal products for the treatment of RA' released by the European Medicines Agency (EMA). Special emphasis was placed by the group on the development of new drugs for the treatment of early RA. In the absence of a clear definition of early RA, it was suggested that clinical investigations in this condition were conducted in disease-modifying antirheumatic drugs naïve patients with no more than 1 year disease duration. The expert group recommended using an appropriate improvement in disease activity (American College of Rheumatology (ACR) or Simplified/Clinical Disease Activity Index (SDAI/CDAI) response criteria) or low disease activity (by any score) as primary endpoints, with ACR/European League Against Rheumatism remission as a secondary endpoint. Finally, as compelling evidence showed that the Disease Acrivity Score using 28-joint counts (DAS28) might not provide a reliable definition of remission, or sometimes even low disease activity, the group suggested replacing DAS28 as a measurement instrument to evaluate disease activity in RA clinical trials. Proposed alternatives included SDAI, CDAI and Boolean criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".