Return of individual genomic research results: are laws and policies keeping step?
Bibliographic record
Abstract
Efforts are underway to harmonise the return of individual results and incidental findings from whole genome sequencing (WGS) across research contexts and countries. We reviewed international, regional and national laws and policies applying to return across 20 countries to identify areas of convergence and divergence. Discrepancies between laws and policies are most problematic where they cannot be reconciled through harmonisation of project-level governance. Rules for the return of results apply at different levels in different jurisdictions (e.g., human subjects research, biobanks, clinical trials, genomic sequencing, and genetic/personal data), complicating comparison. A particular concern for harmonisation are the (often contradictory) rules about when results must, should, may, or must not be returned. Adding confusion are different thresholds for utility (medical, familial, reproductive, and/or personal). The importance of respecting individual choices to know or not know is widely recognised, though some norms emphasise respect for personal preferences. Another troubling observation is that requirements for data quality, variant assessment, and the effective communication of results are evolving in uneven ways. There is a growing gap between researchers with the expertise, infrastructure, and resources to meet these requirements and those without, threatening international collaboration. Best practices for the return of individual genomic results are sorely needed to inform not only the ethical return of results, but also future legislative and policy efforts.
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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 | 0.367 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.023 | 0.038 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.024 | 0.035 |
| Insufficient payload (model declined to judge) | 0.005 | 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".