Ethical Issues of Genetic Information Banks
Bibliographic record
Abstract
Biobanks are an essential and important tool of genetic epidemiology and screening. Biobanks cacould investigates the roles, effects and influences of genetic factors and their interaction with environmental factors such as nutrition, stress, etc (in a broad meaning) for the occurrence of behaviors, health conditions, diseases, and quality of life in human populations. Biobanks aims are to understand the influence and impact of genetics parameters on the development of behaviors, health conditions, diseases, and quality of life, their course and the clinical implications, with the final goal to improve prevention, diagnostics and therapy. The unexpected progress of genetics fields in the last two decades - with respect to the understanding of the meaning of genes for human health, as well as the availability of cost-effective high throughput methods in the lab and techniques, has opened massive opportunities to study genetic factors and their influence in human health condition. In addition, for establishment of a effective biobank access to large cases and samples of patients or from the population is needed. This can be realized via collaboration of several biobanks. Large biobanks with 500,000 or more participants are being established or planned in the UK, Japan, Iceland, Taiwan, Canada, Australia, Italia, Sweden and the US. However, in Germany only two smaller activities are ongoing, KORA-gen in the south and POPGEN in the north. Possibilities to reach larger numbers for Germany, based on existing cohorts or disease networks, are discussed between scientist and government. For the implementation and use of biobanks, stringent ethical, social and legal boundary circumstances have to be taken into account. The opinion of the German National Ethics Council on Biobanks for Research as well as the new advices of the Telematic Platform (TMF), which has been developed in close collaboration with the Data Protection Officers, improve transparency and legal security.
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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 | 0.225 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.030 | 0.026 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".