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Record W4392355648 · doi:10.1016/j.ejmg.2024.104925

A systematic review of the assessment of the clinical utility of genomic sequencing: Implications of the lack of standard definitions and measures of clinical utility

2024· review· en· W4392355648 on OpenAlexaff
Claudia Azuelos, Marc‐Antoine Marquis, Anne‐Marie Laberge

Bibliographic record

VenueEuropean Journal of Medical Genetics · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsGenomic sequencingComputational biologyMEDLINEComputer scienceMedicineBiologyGeneticsGenomeGene

Abstract

fetched live from OpenAlex

PURPOSE: Exome sequencing (ES) and genome sequencing (GS) are diagnostic tests for rare genetic diseases. Studies report clinical utility of ES/GS. The goal of this systematic review is to establish how clinical utility is defined and measured in studies evaluating the impacts of ES/GS results for pediatric patients. METHODS: Relevant articles were identified in PubMed, Medline, Embase, and Web of Science. Eligible studies assessed clinical utility of ES/GS for pediatric patients published before 2021. Other relevant articles were added based on articles' references. Articles were coded to assess definitions and measures of clinical utility. RESULTS: Of 1346 articles, 83 articles met eligibility criteria. Clinical utility was not clearly defined in 19% of studies and 92% did not use an explicit measure of clinical utility. When present, definitions of clinical utility diverged from recommended definitions and varied greatly, from narrow (diagnostic yield of ES/GS) to broad (including decisions about withdrawal of care/palliative care and/or impacts on other family members). CONCLUSION: Clinical utility is used to guide policy and practice decisions about test use. The lack of a standard definition of clinical utility of ES/GS may lead to under- or overestimations of clinical utility, complicating policymaking and raising ethical issues.

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)0.010
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.192
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.298
GPT teacher head0.466
Teacher spread0.168 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2024
Admission routes1
Has abstractyes

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