Informed Choice in Alternative Medicine: Expanding the Doctrine Beyond Conventional Alternative Therapies
Bibliographic record
Abstract
The law of informed choice as presently conceived is dominated by biomedicine. While the law indicates legal and ethical acceptability of options to medical therapies, it is not obvious that alternative medicine falls within this class. The aim of this paper, therefore, is to analyze the doctrine of informed choice in the light of the evolving paradigm of alternative medicine. The primary focus of the paper goes beyond the possible expansion of the doctrine to accommodate safe and efficacious unconventional / alternative therapies and extends to a determination of the criteria by which physicians are required to judge which alternative therapies are safe and effective. The paper also examines the extent to which patient autonomy or the doctrine of express assumption of risk can shield from liability the dual practitioner who uses alternative therapies alongside conventional medicine. I examine this theme alongside the legal implications when the therapy is outside the physician's professional scope of practice.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.058 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".