Second Medical Opinions in End-of-Life Disputes in Critical Care: An Ethics-Based Approach
Bibliographic record
Abstract
Requests for a second medical opinion (SMO) by patients or substitute decision-makers (SDMs) can arise during end-of-life disputes in critical care. Such disagreements between patients or SDMs and physicians often pertain to specific elements of the decision-making process related to withholding or withdrawing of life-sustaining treatments. When these disputes occur in the critical care setting in Canada, practicalities and policy barriers prevent an SDM from obtaining an SMO without support from healthcare providers; moreover, in a majority of these cases the SDM will require the facilitation of a physician who is often the same individual with whom they are in conflict. Institutional and a national society's policy statements propose SMOs as an important component of a conflict resolution process for end-of-life disputes (Bosslet et al. 2015; Singer et al. 2001). However, these policies do not provide specific guidance to physicians on how to fairly consider SMO requests. Given the vulnerable position of patients and their SDMs in the critical care context and in order to promote fairness, physicians should apply consistent standards in deciding whether to facilitate a request for an SMO. To guide physicians' decision-making and inform future policy development, we propose three ethical principles for considering SDM requests for an SMO in critical care at the end of life.
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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 | 0.134 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.020 | 0.103 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.025 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".