Ethical challenges and innovations in the dissemination of genomic data: the experience of the PERSPECTIVE project
Bibliographic record
Abstract
Emmanuelle Lévesque,1 Bartha Maria Knoppers,1 Jacques Simard,2 1Department of Human Genetics, Centre for Genomics and Policy, McGill University, Montréal, 2Genomics Centre, CHU de Québec Research Center, Department of Molecular Medicine, Laval University, Québec City, QC, Canada Abstract: The importance of making genomic data available for future research is now widely recognized among the scientific community and policymakers. In this era of shared responsibility for data dissemination, improved patient care through research depends on the development of powerful and secure data-sharing systems. As part of the concerted effort to share research resources, the project entitled Personalized Risk Stratification for Prevention and Early Detection of Breast Cancer (PERSPECTIVE) makes effective data sharing through the development of a data-sharing framework, one of its goals. The secondary uses of data from PERSPECTIVE for future research promise to enhance our knowledge of breast cancer etiologies without duplicating data-gathering efforts. Despite its benefit for research, we recognize the ethical challenges of data sharing on the local, national, and international levels. The effective management of ethical approvals for projects spanning across jurisdictions, the return of results to research participants, and research incentives and recognition for data production, are but a few pressing issues that need to be properly addressed. We discuss how we managed these issues and suggest how ongoing innovations might help to facilitate data sharing in future genomic research projects. Keywords: data sharing, research ethics, cancer
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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 | 0.246 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.039 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.019 | 0.035 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".