No More Chances for Lost Chances: A Weinribian Response to Weinrib
Bibliographic record
Abstract
Sometimes, patients who were negligently misdiagnosed by their doctors are unable to receive any compensation through tort litigation. This has led to a perception of unfairness, igniting arguments in favour of what is known as the “loss of chance” doctrine. Under this doctrine, patients would be able to claim damages for the lost chances of recovery that they suffered due to negligent misdiagnoses. British and Canadian courts have rejected this doctrine in the medical negligence context on the basis that it does not cohere with tort law principles of injury compensation. Professor Ernest Weinrib, in “Causal Uncertainty” (2016) 36:1 Oxford Journal of Legal Studies 1, has offered an interpretation of loss of chance that he claims would maintain the overall coherence of the tort liability system. In this article I offer a critique of his proposal on the basis that it does not achieve the coherence that it promises. My comments are rooted in the commitment to coherence that professor Weinrib has elucidated in his book The Idea of Private Law (Oxford: Oxford University Press, 2012) so my response to his proposal is, in my view, Weinribian in nature. Drawing on his insights, I comment on how consistency and coherence are related, and how and why these formal values matter to tort law theory and practice.\nParfois, des patients, ayant été négligemment mal diagnostiqués par leur médecin, ne sont pas capables d’obtenir un dédommagement avec une action en responsabilité délictuelle. Ces situations ont mené à la perception d’une injustice, amenant ainsi des arguments en faveur de la théorie de la perte de chance. Sous cette théorie, les patients victimes d’erreurs négligentes de diagnostic pourraient réclamer des dommages- intérêts pour la perte de chance de guérison subie. Les tribunaux britanniques et canadiens ont rejeté cette théorie dans le contexte de la négligence médicale parce qu’elle n’est pas cohérente avec les principes d’indemnisation du droit de la responsabilité délictuelle. Le Professeur Ernest Weinrib, dans « Causal Uncertainty » (2016) 36 : 1 Oxford Journal of Legal Studies 1, a récemment proposé une interprétation de la théorie de la perte de chance pour laquelle il prétend maintenir une cohérence générale avec le droit de la responsabilité délictuelle. Dans cet article, je critique sa proposition au motif qu’elle ne permet pas d’obtenir la cohérence promise. Mes commentaires sont enracinés dans l’engagement du Professeur Weinrib envers la cohérence, qu’il a expliqué dans son livre The Idea of Private Law (Oxford : Oxford University Press, 2012). Mes réponses à sa proposition reflètent donc la nature de sa pensée. En se basant sur ses idées, j’argumente comment la consistance et la cohérence sont liées ainsi que pourquoi ces valeurs sont importantes dans la théorie et dans la pratique du droit de la responsabilité délictuelle.
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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 | 0.044 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.071 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.041 | 0.082 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".