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Record W4297312817 · doi:10.1016/j.jcyt.2022.07.014

Particulates are everywhere, but are they harmful in cell and gene therapies?

2022· article· en· W4297312817 on OpenAlexaff
Samuel A. Molina, Stephanie J. Davies, Dalip Sethi, Steve Oh, Nisha Durand, Michael Scott, Lindsay C. Davies, Klaus Wormuth, Dominic Clarke

Bibliographic record

VenueCytotherapy · 2022
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsInternational Society for Cellular Therapy
Fundersnot available
KeywordsParticulatesGeneBiologyGeneticsEcology

Abstract

fetched live from OpenAlex

Cell and gene therapies (CGTs) are a broad class of revolutionary new treatment modalities with enormous potential to impact multiple intractable diseases. In recent years, the sector has reached a breakthrough point, with unprecedented approvals of cell- and gene-based treatments such as adipose-derived mesenchymal stromal cells (Alofisel, manufactured by Takeda Pharma A/S), chimeric antigen receptor T-cell therapies (e.g., Yescarta, Kite Pharma) and gene therapies (e.g., Luxturna, Spark Therapeutics) and lipid nanoparticle (LNP) vaccines against severe acute respiratory syndrome coronavirus 2 virus (Spikevax by Moderna and Comirnaty by Pfizer-BioNTech).

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)0.003
metaresearch head score (Gemma)0.007
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: Commentary · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0050.009
Open science0.0010.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0110.006

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 head0.289
Teacher spread0.259 · 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
GenreCommentary

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

Citations8
Published2022
Admission routes1
Has abstractyes

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