To Warn or Not to Warn? Genetic Information, Families, and Physician Liability
Bibliographic record
Abstract
Genetic testing raises a number of legal issues. Physicians providing genetic testing may be faced with questions related to privacy, confidentiality, and the duty to warn. Because genetic information is by its very nature familial, genetic test results may have implications for others not privy to the particular physician-patient relationship. This can result in a legal and ethical quandary for the treating physician. This paper addresses questions with respect to genetic testing and the legal obligations of physicians. First, can a physician legally breach doctor-patient confidentiality to inform a family member of a genetic risk? Second, does the physician have a duty to warn the interested third party of that risk? And if the physician fails to warn that party, could s/he be found liable? These questions are addressed here in a comparative fashion, examining Canadian (and, where appropriate, American) common law as well as Quebec civil law. The paper concludes that physicians should be liable for the duty to warn in the context of genetic information only when the risk is serious, imminent, and avoidable.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.013 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".