Physicians and genetic malpractice.
Bibliographic record
Abstract
Primary care physicians are unprepared for the increase in demands for prenatal genetic testing. Often, they do not possess the necessary knowledge, skills or attitudes to provide genetic counselling. Yet, since the demand for prenatal genetic services is growing faster than the number of genetic professionals, the responsibility of genetic counselling will fall to these physicians. Physicians who lack genetic literacy may find themselves the targets of lawsuits for wrongful birth and wrongful life. Wrongful birth and wrongful life claims (in the context of genetics) both assert that but for the physician's negligence, the handicapped child would not have been born. Such medical malpractice suits against physicians exist in the United States, the United Kingdom, Canada and Australia. This paper discusses the case law on wrongful birth/life cases in these four countries. The authors conclude that as the number and availability of prenatal genetic tests increases, so too will the number of genetic malpractice claims, unless the education of physicians and medical students in genetics is promoted, possibly with the Internet as the new educational paradigm.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.013 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 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".