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Record W4221008288 · doi:10.5206/uwomj.v90i1.14009

CRISPR: On the road to restoring sight to the blind

2022· article· en· W4221008288 on OpenAlexvenueno aff
Amy Basilious

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCRISPRGenome editingDiseaseBlindnessCas9MedicineBiologyGeneGeneticsPathologyOptometry

Abstract

fetched live from OpenAlex

CRISPR-Cas9 is a novel gene-editing tool that promised to revolutionize our ability to treat genetic conditions when first introduced. Today, it continues to fuel many areas of health research, ranging from cancers to sickle cell disease to Huntington’s disease.1–3 Vision science researchers immediately saw the potential of CRISPR, with some of the earliest experiments exploring CRISPR as a treatment option for inherited ocular disorders.4 Only a few years later, in March 2020, vision research was again at the forefront of this field.5 A CRISPR therapy was injected into the human body for the first time in an attempt to correct a vision threatening mutation.5 Inherited retinal diseases have traditionally presented therapeutic challenges, but CRISPR is now providing hope for a cure.

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)0.002
metaresearch head score (Gemma)0.002
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: Review
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0220.008

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 head0.013
GPT teacher head0.257
Teacher spread0.244 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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