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Record W3015343921 · doi:10.1089/hum.2020.046

Gene Therapy for Cystic Fibrosis Paved the Way for the Use of Adeno-Associated Virus in Gene Therapy

2020· review· en· W3015343921 on OpenAlexaboutno aff
William B. Guggino, Liudmila Cebotaru

Bibliographic record

VenueHuman Gene Therapy · 2020
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsnot available
Fundersnot available
KeywordsGenetic enhancementAdeno-associated virusCystic fibrosisCystic fibrosis transmembrane conductance regulatorGeneGene deliveryMedicineVirusVirologyBiologyGeneticsInternal medicineVector (molecular biology)

Abstract

fetched live from OpenAlex

Shortly after the cystic fibrosis (CF) gene was identified in 1989, the race began to develop a gene therapy for this condition. Major efforts utilized full-length cystic fibrosis transmembrane conductance regulator packaged into adenovirus, adeno-associated virus (AAV), or liposomes and delivered to the airways. The drive to find a treatment for CF based on gene therapy drove the early stages of gene therapy in general, particularly those involving AAV gene therapy. Since general overviews of CF gene therapy have already been published, this review considers specifically the efforts using AAV and is focused on honoring the contributions of Dr. Barrie Carter.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.196
GPT teacher head0.402
Teacher spread0.205 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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