Raising Genomic Citizens: Adolescents and the Return of Secondary Genomic Findings
Bibliographic record
Abstract
Whole genome and exome sequencing (WGS/WES) techniques raise hope for a new scale of diagnosis, prevention, and prediction of genetic conditions, and improved care for children. For these hopes to materialize, extensive genomic research with children will be needed. However, the use of WGS/WES in pediatric research settings raises considerable challenges for families, researchers, and policy development. In particular, the possibility that these techniques will generate genetic findings unrelated to the primary goal of sequencing has stirred intense debate about whether, which, how, and when these secondary or incidental findings (SFs) should be returned to parents and minors. The debate is even more pronounced when the subjects are adolescents, for whom decisions about return of SFs may have particular implications. In this paper, we consider the rise of "genomic citizenship" and the main challenges that arise for these stakeholders: adolescents' involvement in decisions relating to return of genomic SFs, the types of SFs that should be offered, privacy protections, and communication between researchers and adolescents about SFs. We argue that adolescents' involvement in genomic SF-related decisions acknowledges their status as valuable stakeholders without detracting from broader familial interests, and promotes more informed genomic citizens.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".