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Record W2756992211 · doi:10.1186/s13073-017-0475-4

Key challenges in bringing CRISPR-mediated somatic cell therapy into the clinic

2017· article· en· W2756992211 on OpenAlexaff
Dianne Nicol, Lisa Eckstein, Michael Morrison, Jacob S. Sherkow, Margaret Otlowski, Tess Whitton, Tania Bubela, Kathryn P. Burdon, Drc Chalmers, Sarah Chan, Jac Charlesworth, Christine Critchley, Merlin Crossley, Sheryl de Lacey, Joanne L. Dickinson, Alex W. Hewitt, Joanne Kamens, Erika Kleiderman, Satoshi Kodama, John Liddicoat, David A. Mackey, Ainsley J. Newson, Jane Nielsen, Jennifer K. Wagner, Rebekah McWhirter

Bibliographic record

VenueGenome Medicine · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill UniversitySimon Fraser University
FundersEconomic and Social Research CouncilUniversity of Tasmania
KeywordsCRISPRGenome editingSomatic cellCas9Human geneticsMedicineComputational biologyPalindromeGenomeBiologyBioinformaticsGeneticsGene

Abstract

fetched live from OpenAlex

Genome editing using clustered regularly interspersed short palindromic repeats (CRISPR) and CRISPR-associated proteins offers the potential to facilitate safe and effective treatment of genetic diseases refractory to other types of intervention. Here, we identify some of the major challenges for clinicians, regulators, and human research ethics committees in the clinical translation of CRISPR-mediated somatic cell therapy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.009
Scholarly communication0.0070.007
Open science0.0020.006
Research integrity0.0050.018
Insufficient payload (model declined to judge)0.0070.003

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 head0.333
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations57
Published2017
Admission routes1
Has abstractyes

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