MétaCan
Menu
Back to cohort
Record W2604376600 · doi:10.3389/fgene.2017.00040

Disease Resistance and the Definition of Genetic Enhancement

2017· article· en· W2604376600 on OpenAlexafffund
Derek So, Erika Kleiderman, Seydina B. Touré, Yann Joly

Bibliographic record

VenueFrontiers in Genetics · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill UniversityMcGill Genome Centre
FundersCanadian Institutes of Health Research
KeywordsDiseaseContext (archaeology)Genome editingResistance (ecology)Affect (linguistics)Public healthPsychological interventionMechanism (biology)CRISPRInternet privacyMedicinePsychologyBiologyGeneticsComputer scienceGenePsychiatryCommunication

Abstract

fetched live from OpenAlex

Recent gene editing experiments carried out in human embryos have raised the question of whether interventions like the introduction of a CCR5-Δ32 deletion, which could provide heritable resistance to HIV infection, ought to be considered enhancements. Many authors have used the term "enhancement" in different ways, some based on patients' biomedical outcomes and others on their social context. These classifications are often considered overly imprecise. Nevertheless, the concept of "enhancement" could affect the ways in which these applications are regulated in different jurisdictions, the availability of coverage by insurers or public health care, and the force of public opinion in shaping future policy on gene editing. In order to ethically situate resistance to communicable disease with reference to other techniques, this article provides an overview of its similarities and differences with disease gene therapy in embryos, gene therapy in consenting adults, and vaccination. In discussing key ethical features of CCR5-Δ32 deletion (including its frequency in various populations, biological mechanism, benefits for individuals, and use in previous clinical trials) we offer some potential guideposts for the continuing discussion on how to classify "enhancements" in the age of CRISPR gene editing.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.007
GPT teacher head0.256
Teacher spread0.249 · 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 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
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in GeneticsSame topicCRISPR and Genetic EngineeringFrench-language works237,207