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Record W4402684959 · doi:10.1002/em.22633

Genotoxicity evaluation of gene therapies: A report from the International Workshop on Genotoxicity Testing ( <scp>IWGT</scp> ) 2022

2024· review· en· W4402684959 on OpenAlexaboutno aff
Silvana Libertini, Julie K. Jadlowsky, Thomas A. Lanz, Lauren Mihalcik, Daniella Pizzurro

Bibliographic record

VenueEnvironmental and Molecular Mutagenesis · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsGenotoxicityComputational biologyBioinformaticsBiologyGeneticsMedicine

Abstract

fetched live from OpenAlex

At the 8th International Workshop on Genotoxicity Testing meeting in Ottawa, in August 2022, a plenary session was dedicated to the genotoxicity risk evaluation of gene therapies, including insertional oncogenesis and off-target genome editing. This brief communication summarizes the topics of discussion and the main insights from the speakers. Common themes included recommendations to conduct tailored risk assessments based on a weight-of-evidence approach, to promote data sharing, transparency, and cooperation between stakeholders, and to develop state-of-the-art validated tests relevant to clinical scenarios.

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)0.007
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.030
GPT teacher head0.319
Teacher spread0.289 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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