Personalized medicine in rheumatoid arthritis: Combining biomarkers and patient preferences to guide therapeutic decisions
Bibliographic record
Abstract
The last few decades have seen major therapeutic advancements in rheumatoid arthritis (RA) therapeutics. New disease-modifying antirheumatic drugs (DMARDs) have continued to emerge, creating more choices for people. However, no therapeutic works for all patients. Each has its own inherent benefits, risks, costs, dosing, and monitoring considerations. In parallel, there has been a focus on personalized medicine initiatives that tailor therapeutic decisions to patients based on their unique characteristics or biomarkers. Personalized effect estimates require an understanding of a patient's baseline probability of response to treatment and data on the comparative effectiveness of the available treatments. However, even if accurate risk prediction models are available, trade-offs often still need to be made between treatments. In this paper, we review the history of RA therapeutics and progress that has been made toward personalized risk predictive models for DMARDs, outlining where knowledge gaps still exist. We further review why patient preferences play a key role in a holistic view of personalized medicine and how this links with shared decision-making. We argue that a "preference misdiagnosis" may be equally important as a medical misdiagnosis but is often overlooked.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".