Strengths, weaknesses, and opportunities: three tales demonstrating the use, usefulness, and limitations of Canadian post-market data of adverse drug reactions in real-world settings
Bibliographic record
Abstract
Adverse drug reactions (ADRs) – unintended and harmful events potentially caused by medication use – are a significant concern in healthcare, impacting patient safety, clinical outcomes, and economic burden. Although accurately assessing the risk and economic impact of ADRs in real-world settings is of utmost importance, such efforts are often hindered by the availability and quality of post-market ADR data. This dissertation demonstrates the value of the Canada Vigilance Adverse Reaction (CVAR) database, a repository of Canadian post-market ADR reports maintained by Health Canada, as a potential source of decision-grade information on ADRs. It develops and applies a consistent methodology that can be replicated across different disease areas or specific drugs, leveraging the CVAR database. Through three dissertation projects, this dissertation highlights the strengths, weaknesses, and opportunities of using the CVAR database. In terms of strengths, Dissertation Project 1 illustrates that the CVAR database provides real-world insights into the risk of serious ADR outcomes associated with two brand-name drugs, Remicade and Humira. This project highlights that randomized controlled trials may not accurately reflect the true incidence of certain ADR outcomes in the real-world. Regarding weaknesses, Dissertation Project 2 demonstrates that the CVAR database may not always offer reliable real-world reflections for ADRs, particularly for drugs used to treat diseases with high reporting bias such drugs for mental health. For opportunities, Dissertation Project 3 showcases how ADR reports associated with infliximab and adalimumab in the CVAR databases can help identify potential risk signals for serious ADR outcomes. While analyses based solely on the CVAR database may not be sufficient to draw definitive conclusions, they provide a foundation for generating and testing hypotheses to better understand emerging ADR patterns. Despite limitations such as reporting bias and underreporting, leveraging post-market ADR data remains essential for enhancing drug safety and regulatory decision-making.
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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 | 0.213 | 0.373 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".