Comparison of patient and physician perspectives in the management of rheumatoid arthritis: results from global physician- and patient-based surveys
Bibliographic record
Abstract
BACKGROUND: In order to better understand the perspectives of patients and physicians regarding the treatment and management of rheumatoid arthritis (RA), we present and compare results from a patient-based and a physician-based survey developed by the RA NarRAtive advisory panel. METHODS: The RA NarRAtive initiative is directed by a global advisory panel of 39 healthcare providers and patient organization leaders from 17 countries. A survey of patients self-reporting a diagnosis of RA and a physician-based survey, designed by the advisory panel, were fielded online by Harris Poll from September 2014 to April 2016, and from August 2015 to October 2015, respectively. RESULTS: We present findings from 1805 patients whose RA was primarily managed by a rheumatologist, and 1736 physicians managing patients with RA. Results confirmed that RA carries a substantial disease burden; half of the patients surveyed reported stopping participation in certain activities as a result of their disease. While 90% of physicians were satisfied with their communications with their patients regarding RA treatment, 61% of patients felt uncomfortable raising concerns or fears with their physician. Of the patients providing responses, 52% felt that improved dialogue/discussion would optimize their RA management, and 68% of physicians wished that they and their patients talked more about their RA goals and treatment. Overall, 88% of physicians agreed that patients involved in making treatment decisions tend to be more satisfied with their treatment experience. CONCLUSION: The results of these surveys highlight the impact of RA on patients, and a discrepancy between patient and physician views on communication. Further research, focused on improving patient-physician dialogue, shared goal-setting, and treatment planning, is needed.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".