The doctrine of informed consent: does it exist and has it crossed the Atlantic?
Bibliographic record
Abstract
Use of the term ‘informed consent’ is commonplace in both bioethics and medical law. In the legal context the term may be referred to as ‘the doctrine of informed consent’ but the way that this latter term is used raises doubt as to its value as a legal concept. In this paper I explore the concept of the ‘doctrine of informed consent’ and suggest that it may be useful, but only if limited to the autonomy-driven duty to disclose rather than as a more general referent. Having established the nature of the concept I then consider whether the doctrine - which is applied in a minority of US states, Canada and Australia - has crossed the Atlantic and become part of the law in England and Wales. In particular, I analyse Lord Woolf MR's judgment in Pearce v United Bristol Healthcare NHS Trust and suggest that the law has moved towards the doctrine but that it still falls short of the disclosure required by the reasonable patient standard.
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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 | 0.095 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.149 |
| Scholarly communication | 0.017 | 0.045 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.023 | 0.029 |
| 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".