A Policy Analysis to Inform Options for Medical Device Regulation and Post-Market Surveillance in the Canadian Context
Bibliographic record
Abstract
With ageing populations and rising rates of chronic diseases, medical devices are increasingly vital components of health care. From tongue depressors and stethoscopes to pacemakers and medical robotics, medical devices are numerous, diverse and constantly evolving, integrating medicine, biomechanics, materials, software, and electronics in their development and application (Chen et al., 2018). However, while innovation is generally beneficial for patients, the complexity and diversity of medical devices poses a challenge for regulatory systems across the globe, which are often slow to adapt to the increasing complexity of medical devices, unpredictable risks, and emerging threats to public health (Curfman and Redberg, 2011; Mishra, 2017; Chen et al., 2018). Consequently, with medical device failures and malfunctions appearing in media and academic journals there is a global push for stricter regulatory reform and harmonization of regulatory frameworks internationally (Curfman and Redberg, 2011; Altenstetter, 2012; Maak & Wylie, 2016; Chen et al., 2018). This capstone will review and compare the regulatory frameworks for three jurisdictions (Canada, the United States and the European Union) with an aim of identifying post-market strategies which could be applicable to the Canadian context. Additionally the current and proposed changes to the Canadian medical device regulations will be discussed and analysed across multiple dimensions (burdens on various actors, innovation vs regulatory oversight and health risk protection) and, the 3-I framework will be used to deepen the understanding behind the shift in regulatory approach that Health Canada is working to implement. Lastly, based on the regulatory approaches compared and analysed it will provide an informed opinion on the approach that Health Canada is taking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".