Trends in ethical and legal frameworks for the use of human biobanks
Bibliographic record
Abstract
Numerous studies of genetic epidemiology and post-genomics in respiratory diseases rely on the use of biobanks, defined as organised biological sample collections with associated personal and clinical data. The use of biobanks is increasing and raises several ethical issues. What are the ethical trends and legal frameworks in the post-genomic era? Are there new issues in relation to the developments of techniques and new study designs? How does this affect the clinician's attitudes and relationship with the patients? The main ethical issues encountered are: informed consent; confidentiality; secondary use of samples and data over time; return of results; and data sharing. Different levels and modalities of dealing with such issues are identified and vary from legally binding measures to "soft" regulations, such as ethical recommendations by various committees or professional organisations. A further level of complexity appears with the increasing international dimension of such activities in a context in which national positions vary on those topics. There is a tension between a necessary level of diversity in ethical positions and an indispensable common pedestal of principles and procedures to manage these issues in order to foster research. Current legal and ethical trends favour the facilitation of secondary use of samples, more biobank openness, balanced with a growing attention to dialogue and public/stakeholder consultation, an increased role for research ethics committees and more sophisticated data protection and governance structures.
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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 | 0.154 | 0.220 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.014 |
| 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".