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Record W3206555943 · doi:10.3390/cells10102716

Vaccines against COVID-19: Priority to mRNA-Based Formulations

2021· review· en· W3206555943 on OpenAlexaboutno aff
Steve Pascolo

Bibliographic record

VenueCells · 2021
Typereview
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)VirologyContext (archaeology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicMedicine2019-20 coronavirus outbreakMessenger RNABiologyOutbreakGeneInfectious disease (medical specialty)GeneticsDisease

Abstract

fetched live from OpenAlex

As of September 2021, twenty-one anti-COVID-19 vaccines have been approved in the world. Their utilization will expedite an end to the current pandemic. Besides the usual vaccine formats that include inactivated viruses (eight approved vaccines) and protein-based vaccines (four approved vaccines), three new formats have been validated: recombinant adenovirus (six approved vaccines), DNA (one approved vaccine), and messenger RNA (mRNA, two approved vaccines). The latter was the fastest (authorized in 2020 in the EU, the USA, and Switzerland). Most Western countries have reserved or use the protein vaccines, the adenovirus vaccines, and mRNA vaccines. I describe here the different vaccine formats in the context of COVID-19, detail the three formats that are chiefly reserved or used in Europe, Canada, and the USA, and discuss why the mRNA vaccines appear to be the superior format.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.112
GPT teacher head0.437
Teacher spread0.325 · 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 teacher head, not a consensus.

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

Citations22
Published2021
Admission routes1
Has abstractyes

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