Post-approval drug safety: moving from passive to active pharmacovigilance in Canada
Bibliographic record
Abstract
Adverse drug reactions (ADRs) present heavy burdens for the public health care system, and current pharmacovigilance activities are challenged by the under-reporting of ADRs in spontaneous reporting systems and a lack of incentive for industry to conduct rigorous post-approval research. As part of a new lifecycle approach to drug regulation, Health Canada recently announced plans to develop a new health product vigilance framework that will allocate drug safety resources using prioritization schemes focused on higher risk. These plans include the development of official policy requirements for industry to submit formal Risk Management Plans to Health Canada. This thesis argues that this approach is limited by lack of transparency and standardization, burdens on health care practitioners, and a risk of causing treatment disparities. This thesis presents alternative measures for improving post-market drug safety surveillance through initiatives for enhancing ADR data collection systems. These include the use of electronic health records for automated reporting by health care professionals, the screening of health-related social media sites for ADR reports, and the use of internet-based prescription monitoring systems to solicit ADR reports. This thesis also proposes options for improved post-approval research efforts. These include enhanced legislative authority for Health Canada to mandate post-market research commitments to drug sponsors as conditions of approval, offering extensions on data protection to sponsors in exchange for comparative effectiveness research, implementing mandatory industry-sourced funding for objective third-party research, and ensuring that the Drug Safety and Effectiveness Network contains adequate patient representation. In the current context of limited health care resources, these alternatives merit further consideration, including consultation and validation with relevant stakeholders, in order determine the most value-added methods for improving drug safety surveillance.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".