International ethics harmonization and the global alliance for genomics and health
Bibliographic record
Abstract
On 28 January 2013, funders and researchers met to discuss how to enable rapid progress for biomedicine by lowering global barriers to responsible and secure data sharing [1].Since then, over 130 leading organizations in health care, research and disease advocacy, operating in 40 countries, have signed a letter of intent to work towards the creation of a Global Alliance for Genomics and Health (GA4GH) [2].There are three goals: to enable open standards for interoperability of technology platforms for managing and sharing genomic and clinical data, to provide guidelines and harmonized procedures for privacy and ethics internationally, and to engage stakeholders to encourage responsible sharing of data and of methods.After the mapping and sequencing of the human genome, today's challenge is the integration of genomics data with clinical data.However, interpretation of individual sequences for genomic medicine requires an evidence base of secure data sharing within a framework of core principles.The GA4GH core principles are as follows: respect -protecting secure data sharing and privacy preferences of participants; transparency -ensuring open governance and operations; accountability -promoting best practices in technology, ethics and outreach; inclusivitypartnering and building trust among stakeholders; collaboration -sharing information to advance human health; innovation -developing an ecosystem that accelerates progress; agility -acting swiftly to benefit those with disease.The vision of the GA4GH then is to promote and facilitate the exchange of extensive data on genomic sequences, including clinical annotations, to accelerate progress in genomic medicine, such as for cancer outcomes and targeted therapy, inherited pediatric diseases, other common non-communicable diseases, infectious diseases, and drug responses.In this short paper, we will focus on the international ethics harmonization challenges that the GA4GH faces.
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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 | 0.138 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.021 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 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".