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Record W2460562489 · doi:10.1177/1073110516654123

Raising Genomic Citizens: Adolescents and the Return of Secondary Genomic Findings

2016· article· en· W2460562489 on OpenAlexaff
Maya Sabatello, Paul S. Appelbaum

Bibliographic record

VenueThe Journal of Law Medicine & Ethics · 2016
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsColumbia College
FundersNational Human Genome Research Institute
KeywordsRaising (metalworking)Genomic sequencingExome sequencingCitizenshipExomePsychologyPolitical scienceGenomeGeneticsBiologyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.325
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2016
Admission routes1
Has abstractyes

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