Funding considerations for the disclosure of genetic incidental findings in biobank research
Bibliographic record
Abstract
The use of biobanks in biomedical research has grown considerably in recent years. As a result of the increasing analysis of tissue samples stored in biobanks, there has also been an increase in the probability of discovering-in addition to the research target-incidental findings (IF). We identified 23 laws, policies and guidelines from international, regional and national organizations that provide guidance or identify the need for the disclosure of IF to research participants. We analyzed these instruments to determine their contemplation of the funding considerations for the disclosure of IF, examining their guidance for who discloses and the extent of researcher responsibilities. We found that the available normative documents provide little guidance to researchers and biobanks for how they should address cost and funding concerns associated with IF disclosure. It is therefore essential that the research and policy communities think through the financial implications of imposing an ethical responsibility to disclose IF. Concerted efforts should be made by policymakers, ethicists, researchers, clinicians and research institutions to develop detailed funding recommendations, potentially universal in application, to aid in the disclosure of IF, and we provide recommendations on steps that can be taken to ensure full consideration of these issues.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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; 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".