Providing valid evidence for decision-making: the Drug Safety and Effectiveness Network Methods and Applications Group in Indirect Comparisons (DSEN MAGIC)
Bibliographic record
Abstract
In 2009, the Canadian Institutes of Health Research, Health Canada, and other stakeholders established the Drug Safety and Effectiveness Network (DSEN) to address the paucity of information on drug safety and effectiveness in real-world settings. This unique network invited knowledge users (e.g., policy makers) to submit queries to be answered by relevant research teams. The research teams were launched via open calls for team grants focused in relevant methodologic areas. We describe the development and implementation of one of these collaborating centres, the Methods and Application Group for Indirect Comparisons (MAGIC). MAGIC was created to provide high-quality knowledge synthesis including network meta-analysis to meet knowledge user needs. Since 2011, MAGIC responded to 54% of queries submitted to DSEN. In the past 5 years, MAGIC produced 26 reports and 49 publications. It led to 15 trainees who entered industry, academia, and government. More than 10 000 people participated in courses delivered by MAGIC team members. Most importantly, MAGIC knowledge syntheses influenced practice and policy (e.g., use of biosimilars for patients with diabetes and use of smallpox vaccinations in people with contraindications) provincially, nationally, and internationally.
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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 | 0.705 | 0.820 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".