Informed consent in medical malpractice litigation in Canada and Japan
Bibliographic record
Abstract
The doctrine of informed consent in medical law is increasingly becoming more important in modern society due in part to more sophisticated diagnostic and surgical techniques. The doctrine should both encourage a patient's right of self-determination, which is fundamental and significant in modern societies, and provide practicing doctors with clear and appropriate guidelines. We must achieve these goals simultaneously. In order to do that, we must treat a lot of issues on the doctrine in an integrated and consistent manner. Some of them that arise include the information that should be disclosed to patients, valid exceptions to the doctrine of informed consent, and causation. These issues are examined with a proposal to develop a balanced model of doctor-patient relations and an informed consent doctrine that encourages the patient's right of self-determination, offers clear and proper guidelines for doctors, and eventually improves the quality of medical service.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".