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Record W4205769977 · doi:10.1136/bmjopen-2021-051254

The Canadian National Vaccine Safety Network: surveillance of adverse events following immunisation among individuals immunised with the COVID-19 vaccine, a cohort study in Canada

2022· article· en· W4205769977 on OpenAlexaffabout
Julie A. Bettinger, Manish Sadarangani, Gaston De Serres, Louis Valiquette, Otto G. Vanderkooi, James D. Kellner, Matthew Muller, Karina A. Top, Jennifer E. Isenor, Allison McGeer, Kimberly Marty

Bibliographic record

VenueBMJ Open · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsIzaak Walton Killam Health CentreAlberta Children's HospitalUniversity of CalgarySinai Health SystemCentre Hospitalier Universitaire de SherbrookeUniversité LavalDalhousie UniversityUniversity of TorontoBC Children's HospitalUniversity of British Columbia
FundersGlaxoSmithKlineSeqirusPfizer
KeywordsMedicinePublic healthFamily medicineInstitutional review boardResearch ethicsAdverse effectCohortEnvironmental healthNursingPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: COVID-19 vaccines require enhanced safety monitoring after emergency approval. The Canadian National Vaccine Safety Network monitors the safety of COVID-19 vaccines and provides enhanced monitoring for healthy, auto-immune, immunocompromised, pregnant and breastfeeding populations and allows for the detection of safety signals. METHODS AND ANALYSIS: Online participant reporting of health events in vaccinated and unvaccinated individuals 12 years of age and older is captured in three surveys: 1 week after dose 1, 1 week after dose 2 and 7 months after dose 1. Medically attended events are followed up by telephone. The number, percentage, rate per 10 000 and incident rate ratios with 95% CIs are calculated by health event, vaccine type, sex and in 10-year age groups. ETHICS AND DISSEMINATION: Each study site has Research Ethics Board approvals for the project (UBC Children's & Women's, CIUSSS de l'Estrie-CHUS, Health PEI, Conjoint Health Research Ethics Board, University of Calgary and Alberta Health Services, IWK Health, Unity Health Toronto and CHU de Québec-Université Laval Research Ethics Boards). Individuals are invited to participate in this active surveillance and electronic consent is given before proceeding to each survey. Weekly reports are shared with public health and posted on the study website. At least one peer-reviewed manuscript is produced.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.021
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.372
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.

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

Citations33
Published2022
Admission routes2
Has abstractyes

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