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Record W3126453280 · doi:10.1016/s1470-2045(21)00017-6

SARS-CoV-2 vaccination and phase 1 cancer clinical trials

2021· article· en· W3126453280 on OpenAlexaff
Timothy A. Yap, Lillian L. Siu, Emiliano Calvo, Martijn P. Lolkema, Patricia LoRusso, Jean‐Charles Soria, Ruth Plummer, Johann S. de Bono, Josep Tabernero, Udai Banerji

Bibliographic record

VenueThe Lancet Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersSheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer TherapyNational Center for Advancing Translational SciencesUniversity of Texas MD Anderson Cancer CenterNational Institutes of HealthNational Cancer InstituteNational Institute for Health and Care Research
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VaccinationClinical trialCancerCoronavirus disease 2019 (COVID-19)Virology2019-20 coronavirus outbreakMedicineCancer vaccinePhase (matter)Internal medicineImmunotherapyOutbreakDiseaseInfectious disease (medical specialty)Physics

Abstract

fetched live from OpenAlex

There is now a rapid global roll-out of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines as part of the response to the COVID-19 pandemic.1 Approved SARS-CoV-2 vaccines include those from Pfizer-BioNTech,2 Moderna,3 and Oxford–AstraZeneca,4 but WHO estimates that there are 52 ongoing clinical research projects developing SARS-CoV-2 vaccines.1 Different vaccine mechanisms have been explored using technologies based on messenger RNA (mRNA), synthetic long viral peptides, plasmid DNA, and inactivated, attenuated, or genetically modified viruses.

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)0.010
metaresearch head score (Gemma)0.010
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 score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.415
GPT teacher head0.598
Teacher spread0.183 · 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

Citations12
Published2021
Admission routes1
Has abstractno

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Same venueThe Lancet OncologySame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207