Transportability of nonlocal real-world evidence and its relevance to health technology assessment: a primer
Bibliographic record
Abstract
Real-world evidence (RWE) from outside Canada or the UK is sometimes included in submissions to health technology assessments by Canada's Drug Agency/L'Agence des médicaments du Canada (CDA-AMC) and National Institute for Health and Care Excellence when local data are lacking, particularly in rare diseases. However, differences in population demographics, healthcare systems and clinical practice patterns between different jurisdictions can pose challenges for contextualizing nonlocal data for health technology assessments. This primer outlines the challenges of using nonlocal RWE for decision-making, presents assumptions necessary for transportability of RWE, and describes quantitative methods to address these challenges. This primer is written for a broad audience, including industry stakeholders, researchers and clinicians, who are seeking accessible guidance on the use of nonlocal RWE and developments in the field of transportability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".