Data Collection from Legally Incompetent Subjects: A Paradigm Legal and Ethical Challenge for Population Databanks
Bibliographic record
Abstract
In the last couple of years, several new biobanks have been established with the goal to enable the study of health developments of people and their families over their entire lifetime. These biobanks involve a collection of biological samples as well as other health information, such as clinical, genealogy, overall-health, and life-style data. The establishment of biobanks creates significant legal and ethical challenges, particularly with respect to informed consent. Several authors have discussed these dilemmas, describing how truly meaningful consent is difficult to obtain in the context of biobanks, since they are infrastructures for future research rather than specific projects. Most articles focus on the difficulty of obtaining consent for biobank collections and on some of the practical challenges created by strict application of consent procedures. Less attention has been paid to the fact that many biobanks will inevitably be faced with the fact that several of those who provide samples and consent to the use of these samples and of health information that pertains to them will lose decision-making capacity at one point in time. Indeed, the studies that aim at understanding the complex interaction between genes, environment, and disease in an older population will involve people who have lost or will lose capacity during the study. This paper reviews the legal and ethical implications of a loss of decision-making capacity by research subjects in long-term research associated with large biobanks. It focuses on the questions whether and to what extent researchers can continue to perform research procedures on such subjects and can continue to gather health information on them according to Canadian law and Canadian research ethics guidelines.
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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 | 0.588 | 0.537 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.015 | 0.067 |
| Scholarly communication | 0.034 | 0.049 |
| Open science | 0.012 | 0.027 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 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".