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Record W1525969076 · doi:10.1111/cge.12540

Disclosure of incidental findings in cancer genomic research: investigators' perceptions on obligations and barriers

2014· article· en· W1525969076 on OpenAlexafffundabout
Erika Kleiderman, Denise Avard, A. Besso, Sarah E. Ali‐Khan, Guy Sauvageau, Jean‐Louis Hébert

Bibliographic record

VenueClinical Genetics · 2014
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-RosemontInstitute for Research in Immunology and CancerMcGill UniversityMcGill University and Génome Québec Innovation Centre
FundersCanadian Institutes of Health ResearchGénome Québec
KeywordsThematic analysisPerceptionContext (archaeology)PsychologyQualitative researchAction (physics)Cancer geneticsSocial psychologyMedicineCancerSociologySocial science

Abstract

fetched live from OpenAlex

Although there has been significant research surrounding incidental findings (IFs), the guidelines and information provided to investigators remain unspecific, unclear, and often generalize the course of action to everyone in the field. We explored the perceptions and experiences of investigators regarding the return of IFs in genetic research. Researchers and clinician-researchers were invited to participate in semi-structured telephone interviews in Quebec and Ontario. Twenty professionals participated, and thematic analysis was used to analyze the transcriptions. Four contextual elements emerged: (i) degree of significance of results, (ii) respect for persons, (iii) infrastructure implications, and (iv) professional responsibilities. Our findings demonstrate that all investigators raised similar contextual elements surrounding the return of IFs. However, some nuances in participants' experiences of the understanding of professional responsibilities also emerged. Because of the existing nuances, a one-size-fits-all approach is inappropriate, suggesting that context ought to be considered in decisions about IFs.

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)0.008
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.570
GPT teacher head0.623
Teacher spread0.054 · 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.

Study designObservational
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

Citations18
Published2014
Admission routes3
Has abstractyes

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