Abstract of "An Ethical Analysis of Emerging Models of Consent for Genomics and Biobanking"
Bibliographic record
Abstract
The informed consent process is a primary component of protecting the rights and welfare of individuals involved in human subjects research. This protection is grounded in the concept of the right to autonomy or self-determination, which is a fundamental ethical necessity in demonstrating genuine respect for human integrity and dignity. Each of the international documents that provide guidance for the informed consent process are foundational to ensure respect for persons and works from the presupposition that informed individual choice will always trump views of societal best interests. Consequently, the informed consent process has been a sacrosanct principle of all research in Western nations. Recent advances in the domain of genomics and biobanking have challenged this core principle. Large aggregate population studies require biological samples from large populations for cross-comparative and epidemiological purposes, creating the need for biobanks. With these advances in biobanking, informed consent in its traditional form becomes nearly impossible to implement since the ultimate direction and nature of such research continues to evolve long after original samples are taken. Responsible researchers and health-care professionals are struggling to adhere to the principles of informed consent yet do not wish to stand in the way of highly significant and socially useful research. This poses a true ethical dilemma. In recent years, several pragmatic models of alternative methods for establishing consent have emerged. Yet, the true ethical nature and moral validity of such models have not been well explored. As a means of finding future direction, this presentation analyzes the history of consent in relation to research ethics. It then systematically reviews emerging models of consent for the unique endeavor of genomics and biobanking. A series of recommendations are made based on ethical principles that should be considered when ensuring informed consent as it relates to biobanking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".