Recent Canadian Negligence Decisions Relating to Prenatal Care: Implications for Physicians’ Screening Practices
Bibliographic record
Abstract
This article summarizes several Canadian court decisions from 2015 onward stemming from wrongful birth and wrongful life litigation. Plaintiff success often turns on whether causation is established, on a balance of probabilities, between a physician’s breach of standard of care and the harm to the parents and/or the child later born. Physicians’ failure to offer or order screening or diagnostic tests has been a source of wrongful birth liability, as too can be failure to ensure patient understanding of results. Physicians should ensure that they recommend diagnostic testing when presented with concerning clinical indications in accordance with professional practice guidance. Given non-invasive prenatal screening’s (NIPS) advantages and the threat of wrongful birth liability for failure to discuss this procedure, it is likely to be propelled into an ever more prominent position as a first-choice offering for aneuploidy screening. Appropriately cautious physician behaviour involves discussing and offering NIPS, and also involves ensuring that results are understood. This can reduce physician liability, improve patient reproductive autonomy, and sometimes benefit patient health by preventing or lessening trauma that informed women may opt to mitigate when granted the opportunity.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".