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Record W2000977557 · doi:10.1038/ejhg.2014.108

To disclose, or not to disclose? Context matters

2014· article· en· W2000977557 on OpenAlexafffund
Vasiliki Rahimzadeh, Denise Avard, Karine Sénécal, Bartha Maria Knoppers, Daniel Sinnett

Bibliographic record

VenueEuropean Journal of Human Genetics · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversité de MontréalMcGill University
FundersCanadian Institutes of Health ResearchTerry Fox FoundationFonds de Recherche du Québec - SantéGenome Canada
KeywordsContext (archaeology)Multidisciplinary approachGenetic testingScope (computer science)Corporate governancePsychologyHealth careEngineering ethicsMedicinePolitical scienceSociologyComputer scienceBusinessBiologySocial science

Abstract

fetched live from OpenAlex

Progress in understanding childhood disease using next-generation sequencing (NGS) portends vast improvements in the nature and quality of patient care. However, ethical questions surrounding the disclosure of incidental findings (IFs) persist, as NGS and other novel genomic technologies become the preferred tool for clinical genetic testing. Thus, the need for comprehensive management plans and multidisciplinary discussion on the return of IFs in pediatric research has never been more immediate. The aim of this study is to explore the views of investigators concerning the return of IFs in the pediatric oncology research context. Our findings reveal at least four contextual themes underlying the ethics of when, and how, IFs could be disclosed to participants and their families: clinical significance of the result, respect for individual, scope of professional responsibilities, and implications for the healthcare/research system. Moreover, the study proposes two action items toward anticipatory governance of IF in genetic research with children. The need to recognize the multiplicity of contextual factors in determining IF disclosure practices, particularly as NGS increasingly becomes a centerpiece in genetic research broadly, is heightened when children are involved. Sober thought should be given to the possibility of discovering IF, and to proactive discussions about disclosure considering the realities of young participants, their families, and the investigators who recruit them.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.780
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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

Citations19
Published2014
Admission routes2
Has abstractyes

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