Expectations and educational needs of rheumatologists, rheumatology fellows and patients in the field of precision medicine in Canada, a quantitative cross-sectional and descriptive study
Bibliographic record
Abstract
BACKGROUND: Precision medicine, as a personalized medicine approach based on biomarkers, is a booming field. In general, physicians and patients have a positive attitude toward precision medicine, but their knowledge and experience are limited. In this study, we aimed at assessing the expectations and educational needs for precision medicine among rheumatologists, rheumatology fellows and patients with rheumatic diseases in Canada. METHODS: We conducted two anonymous online surveys between June and August 2018, one with rheumatologists and fellows and one with patients assessing precision medicine expectations and educational needs. Descriptive statistics were performed. RESULTS: 45 rheumatologists, 6 fellows and 277 patients answered the survey. 78% of rheumatologists and fellows and 97.1% of patients would like to receive training on precision medicine. Most rheumatologists and fellows agreed that precision medicine tests are relevant to medical practice (73.5%) with benefits such as helping to determine prognosis (58.9%), diagnosis (79.4%) and avoid treatment toxicity (61.8%). They are less convinced of their usefulness in helping to choose the most effective treatment and to improve patient adherence (23.5%). Most patients were eager to take precision medicine tests that could predict disease prognosis (92.4%), treatment response (98.1%) or drug toxicity (93.4%), but they feared potential negative impacts like loss of insurability (62.2%) and high cost of the test (57.5%). CONCLUSIONS: Our study showed that rheumatologists and patients in Canada are overall interested in getting additional precision medicine education. Indeed, while convinced of the potential benefits of precision medicine tests, most physicians don't feel confident in their abilities and consider their training insufficient to incorporate them into clinical practice.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".