Ethical, Legal, and Social Issues (ELSI) of Responsible Data Sharing Involving Children in Genomics: A Systematic Literature Review of Reasons
Bibliographic record
Abstract
Background Progress in precision medicine relies on the access to, use of, and exchange of genomic and associated clinical data, including from children. The ethical, legal, and social issues (ELSI) of such data access, use, and exchange may be accentuated in the pediatric context due in part to the highly sensitive nature of genomic data, children’s consent-related vulnerabilities, and uncertain risks of reidentification. Systematic analyses of the ELSI and scientific reasons for why and how genomic data may be shared responsibly are, however, limited. Methods: We conducted a modified systematic review of reasons according to Sofaer and Strech to examine the ELSI and scientific reasons for “responsible” sharing of children’s genomic and associated clinical data. Empirical articles, commentaries, and data-sharing policies indexed in Medline, Scopus, Web of Science, and BIOSIS were included in the analysis if they discussed ELSI and were published between 2003 and 2017 in English. Results: One hundred and fifty-one records met our inclusion criteria. We identified 11 unique reasons and 8 subreasons for why children’s genomic data should or should not be shared. Enhancing the prospect of direct and indirect benefits and maximizing the utility of children’s data were top reasons why data should be shared. Inadequate data privacy protection was the leading reason why it should not. We furthermore identified 8 reasons and 30 subreasons that support conditional data sharing, in which recontact for the continued use of children’s data once they reach the age of majority was the most frequently endorsed condition. Conclusions: The complete list of ELSI reasons and responsible conditions provides an evidentiary basis upon which institutions can develop data-sharing policies. Institutions should encourage the sharing of children’s data to advance genomic research, while heeding special reconsent and data protection mechanisms that may help mitigate uncertain longitudinal risks for children and families.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.021 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".