New galaxies in the universe of shared decision-making and rheumatoid arthritis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Implementing shared decision-making (SDM) is a top international priority to improve care for persons living with rheumatoid arthritis. Using SDM tools, such as decision aids improve patients' knowledge and support communication with their clinicians on treatment benefits and risks. Despite calls for SDM in treat-to-target, studies demonstrating effective SDM strategies in rheumatology clinical practice are scarce. Our objective was to identify recent and relevant literature on SDM in rheumatoid arthritis. RECENT FINDINGS: We found a burgeoning literature on SDM in rheumatoid arthritis that tackles issues of implementation. Studies have evaluated the SDM process within clinical consultations and found that uptake is suboptimal. Trials of newly developed patient decision aids follow high methodological standards, but large-scale implementation is lacking. Innovative SDM strategies, such as shared goals and preference phenotypes may improve implementation of treat-to-target approach. Research and patient engagement are standardizing measures of SDM for clinical uses. SUMMARY: Uptake of SDM in rheumatoid arthritis holds promise in wider clinicians' and patients' awareness, availability of decision aids, and broader treat-to-target implementation strategies, such as the learning collaborative. Focused attention is needed on facilitating SDM among diverse populations and those at risk of poorer outcomes and barriers to communication.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".