“What are my options?”: Physicians as ontological decision architects in surgical informed consent
Bibliographic record
Abstract
The aim of a theoretically ideal process of informed consent is to promote the autonomy of the patient and to limit unethical physician paternalism. However, in practice, the nature of the medical profession requires physicians to act as ontological decision architects-based on the medical knowledge that they acquire through their experience and training, physicians ontologically determine a subset of viable courses of action for their patient. What is observed is not an unethical physician limitation or biasing of the patient towards certain treatment options that violates patient autonomy or consciously undermines informed consent, but rather a more foundational paternalism that is necessarily inherent to the physician-patient relationship. In this article we argue for a recognition of this underlying physician paternalism and posit that this necessary paternalism is not a foil to patient autonomy, but rather a foundational aspect of the duties of the medical professional within the physician-patient relationship.
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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 | 0.050 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.097 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".