Development of a Canadian Guidance for reporting real-world evidence for regulatory and health-technology assessment (HTA) decision-making
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Real-world evidence (RWE) can complement and fill knowledge gaps from randomized controlled trials to assist in health-technology assessment (HTA) for regulatory decision-making. However, the generation of RWE is an intricate process with many sequential decision points, and different methods and approaches may impact the quality and reliability of evidence. Standardization and transparency in reporting these decisions is imperative to appraise RWE and incorporate it into HTA decision-making. A partnership between Canadian health system stakeholders, namely, Health Canada and Canada's Drug Agency (formerly the Canadian Agency for Drugs and Technologies in Health), was established to develop guidance for the standardization of reporting of RWE for regulatory and HTA decision-making in Canada. STUDY DESIGN AND SETTING: A collaborative initiative to create structured guidance for RWE reporting in the context of regulatory and HTA decision-making. RESULTS: The developed guidance aims to standardize and ensure transparent reporting of RWE to improve its reliability and usefulness in regulatory and HTA processes. CONCLUSION: This guidance can be adapted for other jurisdictions and will have future extensions to incorporate emerging issues with RWE and HTA decision-making.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | low |
| gpt | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | high |
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 | 0.364 | 0.606 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.033 | 0.023 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.020 | 0.010 |
| Research integrity | 0.025 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".