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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 OpenAlex
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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.437

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 head0.333
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