Substitute consent to data sharing: a way forward for international dementia research?
Bibliographic record
Abstract
A deluge of genetic and health-related data is being generated about patients with dementia. International sharing of these data accelerates dementia research. Seeking consent to data sharing is a challenge for dementia research where patients have lost or risk losing legal capacity. The laws of most countries enable substitute decision makers (SDMs) to consent on behalf of incapable adults to research participation. We compare regulatory frameworks governing capacity, research, and personal data protection across eight countries to determine when SDMs can consent to data sharing. In most countries, an SDM can consent to data sharing in the incapable adult's best interests. Best interests typically include consideration of the individual's previously expressed wishes, values and beliefs; well-being; and inclusion in decision making. Countries differ in how these considerations are balanced. A clear previous consent or refusal to share data typically binds the discretion of an SDM. Though generally permissive, National patchworks of laws and guidelines cause confusion. Clarity on the applicable law and processes to enhance ethical decision making are needed to facilitate substitute consent. Researchers can encourage patients to communicate their research preferences before a loss of capacity, and educate SDMs about their ethical and legal duties. The research community must also continue to promote the importance of data sharing in dementia.
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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 | 0.598 | 0.597 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.033 | 0.093 |
| Open science | 0.009 | 0.036 |
| Research integrity | 0.029 | 0.038 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".