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Record W4387358380 · doi:10.1093/infdis/jiad419

Effectiveness of mRNA COVID-19 Monovalent and Bivalent Vaccine Booster Doses Against Omicron Severe Outcomes Among Adults Aged ≥50 Years in Ontario, Canada: A Canadian Immunization Research Network Study

2023· article· en· W4387358380 on OpenAlexafffundabout
Ramandip Grewal, Sarah A. Buchan, Lena Nguyen, Sharifa Nasreen, Peter C. Austin, Kevin A. Brown, Jonathan B. Gubbay, Nelson Lee, Kevin L. Schwartz, Mina Tadrous, Kumanan Wilson, Sarah E Wilson, Jeffrey C. Kwong

Bibliographic record

VenueThe Journal of Infectious Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of OttawaWomen's College HospitalInstitute for Clinical Evaluative SciencesPublic Health OntarioUniversity Health NetworkUniversity of TorontoOttawa HospitalChildren's & Women's Health Centre of British ColumbiaBruyèreToronto Public Health
FundersDepartment of Family and Community Medicine, University of TorontoCanadian Institutes of Health ResearchCanadian Immunization Research NetworkUniversity of TorontoPublic Health AgencyPublic Health Agency of Canada
KeywordsBivalent (engine)Booster (rocketry)Coronavirus disease 2019 (COVID-19)MedicineBooster doseSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyImmunizationImmunologyChemistryDiseaseAntibodyPhysicsInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

We estimated the effectiveness of booster doses of monovalent and bivalent mRNA COVID-19 vaccines against Omicron-associated severe outcomes among adults aged ≥50 years in Ontario, Canada. Monovalent and bivalent mRNA COVID-19 booster doses provided similar strong initial protection against severe outcomes. Uncertainty remains around waning of protection from these vaccines.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.060
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.323
Teacher spread0.296 · 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

Citations17
Published2023
Admission routes3
Has abstractyes

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