Tissue banking, patient rights, and confidentiality: tensions in law and policy.
Bibliographic record
Abstract
The collection, storage and analysis of tissue samples, including genetic data, has become an increasingly common part of biomedical research. Though there are many scientific justifications for the creation of tissue and DNA databanks, the storage and use of human tissue continues to create legal dilemmas. In this paper, the impact and relevance of existing common law principles are reviewed. It is noted that the Canadian common law rules covering consent and confidentiality may create challenges for the research community. Emerging health information legislation does, however, create a somewhat more lenient research environment, largely because these laws allow, in some circumstances, research on identifiable health information without consent. Nevertheless, conflicts between existing common law, research ethics policy and new health information legislation illustrate profound policy dilemmas created by research involving storage and use of tissue and genetic material.
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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 | 0.051 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.062 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.019 | 0.012 |
| 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".