General Medical Practitioners Acting as Geneticists, a Risky Business?
Bibliographic record
Abstract
The availability of precision medicine tools and approaches has increased considerably over the past decades, propelled by rapid scientific advances in genomics and the popularity of direct-to-consumer genetic testing. Genetic specialists working within public healthcare systems are struggling to meet the growing demand for clinical genetic services. Some experts have suggested that doctors who are not specialized in genetics could take on some of the tasks performed by genetic specialists since they are regularly the first point of contact for people with a genetic predisposition to cancer. However, expanding doctors’ roles may heighten their standard of practice and concomitant medical liability risk to that of genetic specialists. This paper reviews the medical liability regime applicable to this situation through the lens of Canada’s unique bijural legal system. We then compare the state of the law in Canada to that of the United States. According to our findings, unless there is an improvement in the quality of genetic services provided by general practitioners, we could see a growing number of successful liability suits in clinical genetics and precision medicine in the coming years. To prevent this unsatisfactory outcome, additional professional training in core genetic tasks should be made increasingly available to general practitioners and the creation of communities of practice in genetics encouraged. Furthermore, courses introducing medical students to genetics, including its ethical and legal challenges, should be made available and actively promoted within medical curricula.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".