Reporting disease activity in clinical trials of patients with rheumatoid arthritis: EULAR/ACR collaborative recommendations
Bibliographic record
Abstract
OBJECTIVE: To make recommendations on how to report disease activity in clinical trials of rheumatoid arthritis (RA) endorsed by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR). METHODS: The project followed the EULAR standardized operating procedures, which use a three-step approach: 1) expert-based definition of relevant research questions (November 2006); 2) systematic literature search (November 2006 to May 2007); and 3) expert consensus on recommendations based on the literature search results (May 2007). In addition, since this is the first joint EULAR/ACR publication on recommendations, an extra step included a meeting with an ACR panel to approve the recommendations elaborated by the expert group (August 2007). RESULTS: Eleven relevant questions were identified for the literature search. Based on the evidence from the literature, the expert panel recommended that each trial should report the following items: 1) disease activity response and disease activity states; 2) appropriate descriptive statistics of the baseline, the endpoints and change of the single variables included in the core set; 3) baseline disease activity levels (in general); 4) the percentage of patients achieving a low disease activity state and remission; 5) time to onset of the primary outcome; 6) sustainability of the primary outcome; 7) fatigue. CONCLUSION: These recommendations endorsed by EULAR and ACR will help harmonize the presentations of results from clinical trials. Adherence to these recommendations will provide the readership of clinical trials with more details of important outcomes, while the higher level of homogeneity may facilitate the comparison of outcomes across different trials and pooling of trial results, such as in meta-analyses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".