Understanding Patient and Physician Perspectives Regarding Innovative Research in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: The optimal treatment choice for an individual with rheumatoid arthritis (RA) is yet unknown. Although novel approaches, such as pragmatic randomized clinical trials (pRCTs) and biomarker-driven trials are needed to advance personalized RA care, end user views of these approaches have not been extensively studied. This study aimed to gain insight into patients' and physicians' perspectives to enhance the success of future RA research innovations. METHODS: As part of a larger pRCT, we conducted 3 focus groups with 17 patients with RA and 1 focus group with 5 rheumatologists from 2 major university hospitals. The discussions, which revolved around the challenges of innovative research, were transcribed verbatim and thematically analyzed adopting a self-management framework aligned with a patient engagement perspective. RESULTS: Patients' discussions revolved around 3 themes: (1) patients' preferences for information related to medical management decision making; (2) necessary behavior change due to treatment-related challenges; and (3) patient-physician relationship as a foundation for constructively approaching shared decision making. As for physicians, their discussion was organized into 3 themes: (1) the impact of research on medical management of a patient; (2) the feasibility of pRCT and biomarker-driven trials; and (3) how randomization could challenge shared decision making with patients. CONCLUSION: Patients and physicians shared their concerns regarding how being part of research in the setting of clinical care could disrupt day-to-day activities and threaten shared decision making. Understanding patients' and physicians' perspectives regarding pRCT and biomarker-driven trials is key to enhance the success of these research innovations.
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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 | 0.076 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".