Gene Patents Still Alive and Kicking: Their Impact on Provision of Genetic Testing for Long QT Syndrome in the Canadian Public Health-Care System
Bibliographic record
Abstract
Purpose: Although the Supreme Court of the United States limited their availability in Association for Molecular Pathology v. Myriad Genetics, gene patents remain important around the world. We examine the situation in Canada, where gene patents continue to exist, in light of recent litigation relating to familial long QT syndrome (LQTS). Methods: We conducted in-depth semistructured interviews with 25 stakeholders across five Canadian provinces and supplemented this with a case analysis of the litigation. Results: The majority of LQTS testing was carried out outside Canada. Rising costs prompted several provinces to attempt to repatriate testing. However, LQTS gene patents stymied efforts, particularly in provinces where testing was more centralized, increasing costs and lowering innovation. It was in this context that a hospital launched a test case against the LQTS patents, resulting in a novel agreement to free Canadian hospitals from the effects of patents. Conclusion: Our analysis reveals a rapidly evolving genetic test provision landscape under pressure from gene patents, strained budgets and poor collaboration. The litigation resulted in a blueprint for free public use of gene patents throughout Canada's health-care system, but it will only have value if governments are proactive in its use.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".