Data storage and DNA banking for biomedical research: informed consent, confidentiality, quality issues, ownership, return of benefits. A professional perspective
Bibliographic record
Abstract
The purpose of this paper is to formulate a professional and scientific view on the social, ethical, and legal issues that impact on data storage and DNA banking practices for biomedical research in Europe. Many aspects have been considered, such as the requirements for data storage and DNA banking in the public and private sectors in Europe and the issues relating to DNA banking, that is to say the consent requirements for the banking and further uses of DNA samples, their control and ownership, and the return of benefit derived from DNA exploitation to the community. The methods comprise primarily the review of the existing professional guidelines, legal frameworks and other documents related to the data storage and DNA banking practices in public and private sectors in Europe. Then, the issues related to DNA banking were examined during an international workshop organized by the European Society of Human Genetics Public and Professional Policy Committee in Paris, France, 07-08, April, 2000. A total of 50 experts from 12 European countries attended this workshop. It came out that DNA banking for medical and research purposes is indispensable. It facilitates the constitution of large collections, sharing of samples, multiple testing on the same samples, and repeating testing over the years. However, banking organization is complex, requires multiple actors, and concerns are expressed in various countries. International standardization of ethical requirements and policies with regard to DNA banking has been recommended. Such standardization would facilitate a greater protection of individuals as well as future international cooperation in biomedical research.
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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 | 0.242 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.087 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.035 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".