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Management of Incidental Findings in Clinical Genomic Sequencing Studies

2016· other· en· W2375370131 on OpenAlexaff
Sandi Dheensa, Shiri Shkedi‐Rafid, Gillian Crawford, Gabrielle Bertier, Lisa Schonstein, Anneke Lucassen

Bibliographic record

VenueEncyclopedia of Life Sciences · 2016
Typeother
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsEmpirical researchTest (biology)PsychologyDutyInformed consentMedical educationMedicineBiologyPathologyAlternative medicineMathematicsStatisticsLaw

Abstract

fetched live from OpenAlex

Abstract Whole‐genome approaches, which are replacing targeted tests in research and clinical practice, increase the chances of ‘incidental findings’ (IFs) – that is, those unrelated to the reason for the test. IFs raise several challenging questions, such as are researchers obliged to disclose IFs, and does this change if the researcher is also a clinician? How can the clinical significance of IFs be determined, and what significance level should determine disclosure? Could family members be tested to help to clarify significance, and if so, how? What should happen if adult‐onset risks are found in children or prenatally? No consensus currently exists about disclosing IFs from research, or about how participants can be helped to make decisions about and give consent (not) to receive them. We recommend that as more research studies that use genome‐wide tests are launched, longitudinal empirical work be conducted to explore participants' experiences and inform best practice for consent and, where relevant, feedback. Key Concepts Using genome‐wide tests increases the likelihood that findings outside the target area will be made. These are often called ‘incidental findings’ (IFs). Early empirical data show that the chances of finding IFs range between 1% and 7%, depending on the test used. IFs in genetic and genomic medicine differ from those found in imaging or biochemistry because they can predict future risks and risks to family members. Researchers generally are not thought to have a duty of care to research participants, but some experts argue that they ought to return IFs that are clinically valid, medically important and actionable, or even ‘hunt’ for additional findings that fit these criteria. However, research funding for such individualised approaches is often insufficient: protocols do not always include quality assurance procedures, and research teams lack health professionals who can communicate findings to participants. The research‐clinical practice boundary is often blurry: participation to some research studies is offered to patients in the clinic and some promise clinical feedback (e.g. the United Kingdom's 100 000 Genomes Project). Clinical validity is difficult to determine in both research and clinical practice, and this will not necessarily become easier with time, meaning upon discovery, IFs are better thought of as ‘potential incidental findings’. Paediatric IFs introduce more complexity: guidelines and legislation suggest that testing children for adult‐onset conditions should be deferred, but with IFs, the question is not about whether to test the child, but whether to reveal information already found. Prenatal IFs raise the additional issue that women might terminate pregnancies based on information that has uncertain significance. Research about participants' preferences shows that people want to know about all IFs, but this research has been hypothetical and cross‐sectional; thus, satisfaction with real‐life decisions has not been explored. The problem of hypothetical decision making is also relevant in real‐life research settings: at the time of consent, participants are unlikely to have thought about their choices or their implications. Empirical research is thus required to determine best practice in consent and disclosure.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.043
metaresearch head score (Gemma)0.219
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.219
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0020.003
Scholarly communication0.0040.005
Open science0.0030.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.352
GPT teacher head0.563
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations6
Published2016
Admission routes1
Has abstractyes

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