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Record W4302011827 · doi:10.1093/infdis/jiac403

The Relationship Between Anti-Spike SARS-CoV-2 Antibody Levels and Risk of Breakthrough COVID-19 Among Fully Vaccinated Adults

2022· article· en· W4302011827 on OpenAlexafffund
Michael Asamoah-Boaheng, David A. Goldfarb, Mohammad Ehsanul Karim, Sheila F. O’Brien, Nechelle Wall, Steven J. Drews, Vilte Barakauskas, Agatha N. Jassem, Brian Grunau

Bibliographic record

VenueThe Journal of Infectious Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of AlbertaCanadian Blood ServicesUniversity of OttawaCentre for Advancing Health OutcomesIsland HealthUniversity of British ColumbiaMemorial University of Newfoundland
FundersMichael Smith Health Research BCGovernment of Canada
KeywordsAntibodySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyCoronavirus disease 2019 (COVID-19)CoronavirusImmunologyMedicineAntigen2019-20 coronavirus outbreakDiseaseBiologyOutbreakInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The relationship between antibodies to wild-type severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens and the risk of breakthrough infections is unclear, especially during circulation of the Omicron strain. We investigated the association of anti-spike and anti-receptor binding domain antibody levels and the risk of subsequent breakthrough coronavirus disease 2019 (COVID-19). We included adult paramedics from an observational cohort study who received 2 mRNA vaccines but did not have COVID-19 before the blood collection. Higher postvaccination antibody levels to wild-type SARS-CoV-2 antigens were associated with a reduced risk of COVID-19. Further research into clinical utility of antibody levels, to inform a threshold for protection and timing of boosters, should be prioritized.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.369
Teacher spread0.322 · 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

Citations23
Published2022
Admission routes2
Has abstractyes

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