Reading the Future?: Legal and Ethical Challenges of New Predictive Genetic Testing
Bibliographic record
Abstract
The mapping of the Human Genome has been touted as the beginning of a new scientific era. In medicine, it is expected to bring with it the widespread use of “predictive genetic testing” a term used to describe both pre-symptomatic testing and susceptibility testing on healthy individuals. In the last decade, several predictive genetic tests have been developed, primarily for single-gene disorders. While progress in understanding the precise role of genetics in more complex disorders has been slower than expected, research into the development of genetic tests for these disorders also continues.\nPredictive genetic tests can be of significant value for patients and for public health strategies, but many ethical and legal issues are associated with the development and use of such tests. This book identifies and examines these issues and makes recommendations that will be of value to policy makers, regulators and law reformers. It is also a source of information for all those interested in the important ethical, social and legal issues raised by the new genetics.\nThis book focuses on the Canadian context, but numerous international and comparative policy reports, studies and scholarly articles that discuss many of the same issues are also canvassed. The issues analyzed include: - why genetic information merits special attention - the need to develop of a regulatory review structure to assess the validity and value of genetic tests - genetic discrimination and stigmatization, including in employment, insurance, financial institutions, adoption, education and health services - regulation of genetic research to protect the interests of human subjects - access to genetic services, especially in a publicly-funded health care system - patents on genes, including Canada's obligations under international patent law and the impact of patents on the research and clinical environment and on clinical genetic testing services - commercialization and direct marketing of genetic testing - regulation and liability of genetic counsellors - clinical issues, including consent, privacy, disclosure, testing of minors and persons of reduced capacity - storage of genetic material and information
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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 | 0.041 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.028 | 0.039 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".