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Record W3214359021 · doi:10.1016/j.ymthe.2021.10.025

Eliminating Panglossian thinking in development of AAV therapeutics

2021· article· en· W3214359021 on OpenAlexaff
Radosław Kaczmarek, Glenn F. Pierce, Declan Noone, Brian O’Mahony, David Page, Mark W. Skinner

Bibliographic record

VenueMolecular Therapy · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsCanadian Hemophilia Society
FundersBayer FundTakeda Pharmaceutical CompanyTakeda Pharmaceuticals U.S.A.Novo NordiskSanofiBayerNational Headache FoundationNational Hemophilia Foundation
KeywordsFood and drug administrationAdvisory committeeVector (molecular biology)Genetic enhancementMedicineToxicityGene transferGenomeGeneVirologyBiotechnologyBiologyPharmacologyPolitical scienceGeneticsInternal medicine

Abstract

fetched live from OpenAlex

The US Food and Drug Administration (FDA) held a 2-day meeting (September 2–3, 2021) of the Cellular, Tissue and Gene Therapies Advisory Committee to consider toxicity risks of adeno-associated virus (AAV) vector-associated gene therapy products including a review of oncogenicity risks due to vector genome integration and safety issues identified during preclinical and/or clinical evaluation.

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)0.006
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.009
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.316
Teacher spread0.287 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations15
Published2021
Admission routes1
Has abstractno

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