DNA databanks and consent: A suggested policy option involving an authorization model
Bibliographic record
Abstract
BACKGROUND: Genetic databases are becoming increasingly common as a means of determining the relationship between lifestyle, environmental exposures and genetic diseases. These databases rely on large numbers of research subjects contributing their genetic material to successfully explore the genetic basis of disease. However, as all possible research questions that can be posed of the data are unknown, an unresolved ethical issue is the status of informed consent for future research uses of genetic material. DISCUSSION: In this paper, we discuss the difficulties of an informed consent model for future ineffable uses of genetic data. We argue that variations on consent, such as presumed consent, blanket consent or constructed consent fail to meet the standards required by current informed consent doctrine and are distortions of the original concept. In this paper, we propose the concept of an authorization model whereby participants in genetic data banks are able to exercise a certain amount of control over future uses of genetic data. We argue this preserves the autonomy of individuals at the same time as allowing them to give permission and discretion to researchers for certain types of research. SUMMARY: The authorization model represents a step forward in the debate about informed consent in genetic databases. The move towards an authorization model would require changes in the regulatory and legislative environments. Additionally, empirical support of the utility and acceptability of authorization is required.
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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 | 0.148 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.017 | 0.041 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.053 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 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; 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".