A Legal Duty of Genetic Recontact in Canada
Bibliographic record
Abstract
Our understanding of the clinical significance of genomic data is rapidly evolving. The meaning of a patient’s test results can therefore change over time. Reanalysis of genomic data over time and patient recontact offer an opportunity to improve patient health. But are physicians legally responsible to do so? Professional associations worldwide are outlining best practices for genetic recontact. To inform Canadian guidelines and courts faced with this issue, we review Canadian case law to determine if there is a likely doctrinal basis for judicial recognition of a duty to recontact in genetics. Foreign guidelines or malpractice case law may not adequately reflect the peculiarities of Canada’s diverse legal and public health systems. A threshold consideration is the duration of the physician-patient relationship, seeing as physicians do not generally owe legal duties to former patients. This legal relationship endures according to the need for continued care as well as the subjective perspectives of both physician and patient. Satisfying these criteria in genetics can be difficult because of interpretative uncertainty or the absence of definitive intervention. Moreover, coordination of genetic analysis, communication, and follow-up care between healthcare professionals is complex, leading to problems of incomplete hand-off between laboratories, specialists, and primary care providers. The key challenge for plaintiffs will be to establish fault, that is, breach of a duty. Physicians in Canada traditionally have duties to diagnose, inform, follow-up and of confidentiality. We conclude that a legal duty of genetic recontact is only likely in specific circumstances where physicians acquire updated genetic information of clear health significance. This remains unlikely unless health systems or laboratories commit to systemic and adaptive reanalysis. This may change with the confluence of whole genome testing and advanced health information technologies (HIT). Whole genome sequences include millions of individual genetic variants and in turn, millions of opportunities for adaptive reinterpretation. HIT enables data sharing between laboratories, automated reanalysis of genomic test results, and new lines of communication with physicians and patients. Fundamentally, it is only health systems or institutions that can provide the infrastructure needed to adapt patient care in step with an evolving genetic knowledgebase.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.009 |
| 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".