EULAR points to consider for conducting clinical trials and observational studies in individuals at risk of rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Despite growing interest, there is no guidance or consensus on how to conduct clinical trials and observational studies in populations at risk of rheumatoid arthritis (RA). METHODS: An European League Against Rheumatism (EULAR) task force formulated four research questions to be addressed by systematic literature review (SLR). The SLR results informed consensus statements. One overarching principle, 10 points to consider (PTC) and a research agenda were proposed. Task force members rated their level of agreement (1-10) for each PTC. RESULTS: Epidemiological and demographic characteristics should be measured in all clinical trials and studies in at-risk individuals. Different at-risk populations, identified according to clinical presentation, were defined: asymptomatic, musculoskeletal symptoms without arthritis and early clinical arthritis. Study end-points should include the development of subclinical inflammation on imaging, clinical arthritis, RA and subsequent achievement of arthritis remission. Risk factors should be assessed at baseline and re-evaluated where appropriate; they include genetic markers and autoantibody profiling and additionally clinical symptoms and subclinical inflammation on imaging in those with symptoms and/or clinical arthritis. Trials should address the effect of the intervention on risk factors, as well as progression to clinical arthritis or RA. In patients with early clinical arthritis, pharmacological intervention has the potential to prevent RA development. Participants' knowledge of their RA risk may inform their decision to participate; information should be provided using an individually tailored approach. CONCLUSION: These consensus statements provide data-driven guidance for rheumatologists, health professionals and investigators conducting clinical trials and observational studies in individuals at risk of RA.
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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 | 0.527 | 0.625 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.152 | 0.058 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".