We don’t need unilateral DNRs: taking informed non-dissent one step further
Bibliographic record
Abstract
Although shared decision-making is a standard in medical care, unilateral decisions through process-based conflict resolution policies have been defended in certain cases. In patients who do not stand to receive proportional clinical benefits, the harms involved in interventions such as cardiopulmonary resuscitation seem to run contrary to the principle of non-maleficence, and provision of such interventions may cause clinicians significant moral distress. However, because the application of these policies involves taking choices out of the domain of shared decision-making, they face important ethical and legal problems, including a recent challenge to their constitutionality. In light of these concerns, we suggest a re-conceptualization of informed non-dissent as an alternative approach in cases where the application of process-based policies is being considered. This clinician-directed communication model still preserves what is valuable in such policies and salvages professional integrity, while minimising ethical and legal challenges.
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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 | 0.104 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.065 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.021 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".