International Genomic Data Sharing by Health Technologies Industries: Points to Consider
Bibliographic record
Abstract
This document outlines Points to Consider (PtC) for the responsible sharing of human genomic and health data internationally by Health Technologies Industries (HTI). HTI can contribute unique resources, technologies, and expertise to translating genomic discoveries into improvements in human health. International data sharing can further accelerate research and innovation. It can strengthen statistical power and reproducibility, facilitate collaboration and creative re-use of real-world evidence, increase the representativeness of precision medicine databases, and power AI approaches (including machine learning, deep learning and predictive modeling) that support genomic interpretation and clinical decision-making. Yet, research, innovation, and data sharing to advance precision medicine also raise important ethical issues, which include risks to the welfare and privacy of sequenced individuals, their families, and communities. The legal and policy landscape relating to data sharing is rapidly evolving. Relevant norms apply in areas of data privacy and protection law; AI law, governance, and ethical principles; research ethics regulations; and data sharing policies. This PtC tailored for HTI builds on the GA4GH’s Framework for Responsible Sharing of Genomic and Health-related Data (2014, re-approved 2019) and subsequent policies. The Framework is founded on human rights, aiming in particular to activate the right of everyone to share in scientific advancement and its benefits. Relevant Core Elements include: Transparency; Accountability; Data Quality and Security; Privacy, Data Protection and Confidentiality; Risk-Benefit Analysis; and Recognition and Attribution. Implementation of this PtC requires careful attention to the particular context – including the relevant jurisdictions, applicable laws and policies, sectors, companies, data sharing activities, and types of health and genomic data. The PtC is accompanied by Explanatory Notes (Appendix A) and issue-driven Briefs (Appendix B) to set the international context.
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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 | 0.175 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.048 | 0.040 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.037 | 0.032 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".