MétaCan
Menu
Back to cohort
Record W4376608471 · doi:10.1038/s41587-023-01816-6

Author Correction: Deconvolution of clinical variance in CAR-T cell pharmacology and response

2023· erratum· en· W4376608471 on OpenAlexaff
Daniel C. Kirouac, Cole Zmurchok, Avisek Deyati, Jordan Sicherman, Chris T. Bond, Peter W. Zandstra

Bibliographic record

VenueNature Biotechnology · 2023
Typeerratum
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British ColumbiaHatch (Canada)
Fundersnot available
KeywordsDeconvolutionVariance (accounting)Computational biologyPharmacologyClinical pharmacologyMathematicsBiologyStatisticsEconomics

Abstract

fetched live from OpenAlex

In the version of this article initially published, there were conversion errors in the display equations presented in the “Model structure and assumptions” section of the Methods and the “Model structural assessment” section of the Supplementary Information. The errors have been corrected in the HTML and PDF versions of the article.

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)0.009
metaresearch head score (Gemma)0.126
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.114
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.126
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0040.003
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.1140.055

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 head0.032
GPT teacher head0.412
Teacher spread0.379 · 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
GenreOther

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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueNature BiotechnologySame topicCAR-T cell therapy researchFrench-language works237,207