As the Pendulum SwingsâMedical Products Class Actions Litigation in Canada: Recent Developments
Bibliographic record
Abstract
During the relatively short history of class proceedings in Canada, developers and manufacturers of medical devices and pharmaceuticals (“medical products”), including medical products designed for patients with diabetes, have found themselves at the receiving end of a significant number of class action claims. As a result, medical products litigation has become the battleground for some of the most significant developments in Canadian class actions law. This article provides a broad overview of some of the most significant developments. The authors pay particular attention to developments regarding the test for class action certification and consider whether high-profile dismissals of certification motions represent a trend toward raising the threshold for plaintiffs seeking to obtain certification of a proposed class action. The authors also consider a decision arising out of a lengthy class action common issues trial in which the medical device company was victorious. In the authors’ view, the class action pendulum in Canada, particularly as it relates to medical products claims, remains in motion. It behooves all affected players to keep their eye on this ball with rapt attention to see where it may move next.
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 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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".