MétaCan
Menu
Back to cohort
Record W3132921411 · doi:10.1186/s12979-021-00219-y

Unanticipated efficacy of SARS-CoV-2 vaccination in older adults

2021· article· en· W3132921411 on OpenAlexaff
Graham Pawelec, Janet E. McElhaney

Bibliographic record

VenueImmunity & Ageing · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsImmunosenescenceMedicineCoronavirus disease 2019 (COVID-19)VaccinationClinical trialSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakRandomized controlled trialPandemicImmunologyVirologyInfectious disease (medical specialty)DiseaseOutbreakImmune systemInternal medicine

Abstract

fetched live from OpenAlex

The rapidity with which vaccines against COVID-19 have been developed and tested is unprecedented. As classically the case with randomized clinical trials, many studies excluded older adults. However, given the early realisation that senior citizens were most highly susceptible to COVID, older individuals have been included in licensing trials under these unusual conditions. The recently published results from the Comirnaty Vaccine (BNT162b) trial unexpectedly documented that vaccine efficacy was equally exceptionally high in older and younger adults. These extremely encouraging trial results with a neoantigen vaccine may suggest the beginning of a paradigm shift in our view of the impact of immunosenescence on vaccination against novel infectious diseases.

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)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.056
GPT teacher head0.379
Teacher spread0.323 · 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 designObservational
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

Citations25
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueImmunity & AgeingSame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207