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Record W4399138627 · doi:10.1038/s41431-024-01640-9

Beyond severity: utility as a criterion for setting the scope of RGCS

2024· article· en· W4399138627 on OpenAlexaff
Lisa Dive, Anne‐Marie Laberge, Lucinda Freeman, Eline M. Bunnik

Bibliographic record

VenueEuropean Journal of Human Genetics · 2024
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsScope (computer science)Context (archaeology)Expected utility hypothesisGenetic testingProperty (philosophy)Value (mathematics)Risk analysis (engineering)Computer scienceActuarial scienceMedicineBiologyBusinessEconomicsGeneticsMachine learningMathematical economics

Abstract

fetched live from OpenAlex

Reproductive genetic carrier screening (RGCS) allows prospective parents to identify and act upon their chances of having a child with a genetic condition. In deciding which genetic conditions to include in RGCS, severity is often used as a criterion. However, the concept is inherently complex, subjective and multidimensional, and determinations of severity will remain intractably contested. We propose the concept of utility as a criterion for setting the scope of RGCS, and put forward two central arguments for doing so. First, utility is a more appropriate and effective concept as it responds to context and makes an explicit connection between the purpose of RGCS and the value of information obtained for that purpose: namely, to facilitate reproductive decision-making. Utility comprises both clinical and personal utility, and varies according to the availability and accessibility of reproductive options, including pre-implantation genetic testing, prenatal genetic diagnosis, and termination of pregnancy. Second, there are ethical reasons for preferring utility over severity. Utility is a property of the information gleaned from RGCS, while severity is a property of a genetic condition or of an instance of this condition in a person. While consideration of the severity of genetic conditions is not lost when focusing on utility, the need to rely on value judgements regarding the quality of life of people who live with genetic conditions is circumvented. Therefore, utility should replace severity as justification for the inclusion of genetic conditions in RGCS programmes.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.031
GPT teacher head0.317
Teacher spread0.286 · 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 designOther design
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

Citations11
Published2024
Admission routes1
Has abstractyes

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