The Deceased, Public Health, and Research: Proposing Legal Reforms
Bibliographic record
Abstract
There is little guidance concerning biomedical research using tissues from deceased individuals. Unique ethical and legal challenges gained visibility during the coronavirus disease 2019 (COVID-19) pandemic, when important studies using genome sequencing required access to biological materials from deceased individuals. These studies proposed to determine whether specific genomic profiles were associated with important disease outcomes. Such research has previously required consent from next-of-kin or other surrogate decision makers. Ethics waivers for such consent vary within Canada. In Ontario, research ethics boards can grant waivers of consent if the Tri-Council Policy Statement-2 conditions are met. These include that the individual is not harmed, that the materials are essential to the research, and that privacy will be protected. Conversely, in Quebec, Civil Code article 22 imposes an obligation on researchers to seek consent from next-of-kin or another surrogate decision maker with no option for waivers. It became evident to researchers that these standards can sometimes impose an impracticable balance of risks and benefits, especially in public health emergencies. We seek to establish why and when consent requirements should be waived for public health and research involving the tissues of deceased individuals.
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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 | 0.172 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.057 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.058 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 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".