Nontherapeutic research with imminently dying and recently deceased study populations: addressing practical and ethical challenges
Bibliographic record
Abstract
Because of a paucity of high-quality evidence, the updated Canadian Death Determination Guidelines featured in this month's Special Issue of the Journal include several recommendations based on low to moderate certainty of evidence or expert opinion. 1 In generating its recommendations, the guideline development group identified numerous knowledge gaps. 2 Many of these suggest research questions answerable only through nontherapeutic studies involving imminently dying or recently deceased adult and pediatric patients in controlled intensive care unit environments.While advancing the science of death determination is in the interest of patients, families, health care providers, health care institutions, and society, there are currently no dedicated Canadian ethical guidelines addressing the substantial challenges of research with imminently dying or recently deceased patients (Table 1).Indeed, to our knowledge, nor are there authoritative international guidelines for research with these populations, perhaps owing to the novelty of these areas of research.Uncertainty regarding the ethics of research with the imminently dying and recently deceased has hindered research into important scientific questions.Accordingly, below we explore the ethical and practical challenges of research with these populations, highlight existing guidance where available, and point to areas where further guidance is needed. Ethical lacunaeThe principles of respect for persons, justice, and beneficence guide all research with human participants.3 These principles ground moral rules to which researchers must adhere (Table 2).
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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 | 0.483 | 0.623 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".