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Record W2106606850 · doi:10.1136/medethics-2011-100273

Individual genetic and genomic research results and the tradition of informed consent: exploring US review board guidance

2012· article· en· W2106606850 on OpenAlexaboutno aff
Christian Simon, Laura Shinkunas, Debra Brandt, Janet K. Williams

Bibliographic record

VenueJournal of Medical Ethics · 2012
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
FundersNational Center for Research ResourcesNational Human Genome Research Institute
KeywordsInformed consentReadabilityInstitutional review boardPsychologyPublic relationsMedical educationPolitical scienceMedicineAlternative medicineComputer sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Genomic research is challenging the tradition of informed consent. Genomic researchers in the USA, Canada and parts of Europe are encouraged to use informed consent to address the prospect of disclosing individual research results (IRRs) to study participants. In the USA, no national policy exists to direct this use of informed consent, and it is unclear how local institutional review boards (IRBs) may want researchers to respond. OBJECTIVE AND METHODS: To explore publicly accessible IRB websites for guidance in this area, using summative content analysis. FINDINGS: Three types of research results were addressed in 45 informed consent templates and instructions from 20 IRBs based at centres conducting genomic research: (1) IRRs in general, (2) incidental findings (IFs) and (3) a broad and unspecified category of 'significant new findings' (SNFs). IRRs were more frequently referenced than IFs or SNFs. Most documents stated that access to IRRs would not be an option for research participants. These non-disclosure statements were found to coexist in some documents with statements that SNFs would be disclosed to participants if related to their willingness to participate in research. The median readability of template language on IRRs, IFs and SNFs exceeded a ninth-grade level. CONCLUSION: IRB guidance may downplay the possibility of IFs and contain conflicting messages on IRR non-disclosure and SNF disclosure. IRBs may need to clarify why separate IRR and SNF language should appear in the same consent document. The extent of these issues, nationally and internationally, needs to be determined.

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)0.139
metaresearch head score (Gemma)0.513
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.549
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1390.513
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.014
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 head0.821
GPT teacher head0.624
Teacher spread0.197 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2012
Admission routes1
Has abstractyes

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