Canadian, English, and Australian Judges Faced with Causal Uncertainty in Medical Liability Cases (Le Juge Canadien, Anglais Et Australien Devant L’Incertitude Causale En Matière De Responsabilité Médicale)
Bibliographic record
Abstract
English Abstract: In this paper the author discusses the treatment of uncertain causation in medical liability cases decided by the highest courts in Canada, England, and Australia. The author specifically focuses on judicial reasoning with regard to three particular concepts: creation of or increased risk, knowledge of causal facts, and loss of chance. A review of this case law reveals that these courts generally decline to find causation or liability proven on the basis that the defendant increased the risk of injury. Also marginal are those instances in which causation has been inferred on the basis of the particular knowledge of the defendant. The Supreme Court of Canada has drawn such inferences only where the defendant has particular knowledge of the causal facts and, by his or her own negligence, has rendered the evaluation of the causal link impossible. Finally, this text addresses the recent treatment of loss of chance in medical liability cases, observing that all the courts studied have rejected this approach. This brief review sheds light on the observation that, despite being sensitive to difficulties plaintiffs face in proving uncertain causation in medical liability cases, the courts studied continue to take orthodox approaches to causation. This study also allows the reader to observe the important place that comparative law has played in the high court decisions rendered in this field.French Abstract: IL’auteure examine le traitement de l’incertitude causale dans les affaires de responsabilite medicale decidees par les tribunaux de derniere instance au Canada, en Angleterre et en Australie. Elle se penche notamment sur le raisonnement judiciaire a l’egard de trois concepts particuliers, soit la creation ou l’augmentation du risque, la creation fautive de l’incertitude causale et la perte de chance. Une analyse de cette jurisprudence permet de constater que la possibilite de trouver la causalite ou la responsabilite prouvee sur la base de l’augmentation du risque de prejudice par le defendeur est generalement rejetee en matiere medicale. Quant au raisonnement permettant d’inferer la causalite lorsque le defendeur possede une connaissance particuliere des faits et que, par sa faute, il a rendu impossible l’evaluation du lien causal, il fut marginalement applique par la Cour supreme du Canada. Enfin, le texte s’attarde au traitement recent de la perte de chance en matiere medicale et constate son rejet par tous les tribunaux d’instance superieure etudies. Cette breve etude montre que, malgre l’expression d’une sensibilite a l’egard des difficultes rencontrees par les demandeurs dans la preuve de la causalite medicale en presence d’incertitude, les approches judiciaires dans les ressorts etudies demeurent orthodoxes. L’etude permet egalement de realiser la place importante que tient le droit compare dans les decisions recentes des tribunaux superieurs dans le domaine.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".