Rheumatoid Arthritis: The Continuum of Disease and Strategies for Prediction, Early Intervention, and Prevention
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is known to include a pre-RA stage that can be defined as the presence of familial or genetic risk factors, biomarker abnormalities (eg, anticitrullinated protein antibodies [ACPA]), symptoms, and even abnormal imaging findings prior to the development of the onset of clinical RA with inflammatory arthritis that is apparent on physical examination. Indeed, there are multiple completed or ongoing retrospective case-control as well as prospective observational studies to identify the key biologic drivers of disease. Further, building on the predictive ability of combinations of biomarkers, symptoms, and imaging for future RA, there are multiple clinical trials completed, underway, or in development to identify approaches that may prevent, delay, or ameliorate future clinical RA in at-risk individuals. Importantly, however, although an effective preventive intervention has not yet been identified, at-risk individuals are being increasingly identified in clinical care; this presents a challenge of how to manage these individuals in clinical practice. This review will discuss the current understanding of the biology and natural history of RA development, nomenclature, and current models for prediction of future RA, as well as evaluate the current and ongoing clinical prevention trials with the overall goal to provide insights into the challenges and opportunities in the field of RA prevention. Moreover, this review will provide up-to-date options for clinical management of individuals at risk for RA.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".