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Record W4213418527 · doi:10.1101/2022.02.22.22271358

Safety of COVID-19 vaccines in pregnancy: a Canadian National Vaccine Safety (CANVAS) Network study

2022· preprint· en· W4213418527 on OpenAlexafffundabout
Manish Sadarangani, Phyumar Soe, Hennady P. Shulha, Louis Valiquette, Otto G. Vanderkooi, James D. Kellner, Matthew Muller, Karina A. Top, Jennifer E. Isenor, Allison McGeer, Mike Irvine, Gaston De Serres, Kimberly Marty, Julie A. Bettinger

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsUniversité LavalInstitut National de Santé Publique du QuébecAlberta Children's HospitalUniversity of CalgarySinai Health SystemCentre Hospitalier Universitaire de SherbrookeDalhousie UniversityUniversity of TorontoBC Children's HospitalBC Centre for Disease ControlUniversity of British Columbia
FundersCanadian Institutes of Health ResearchBC Children's HospitalMichael Smith Health Research BCPublic Health AgencyCanadian Child Health Clinician Scientist ProgramChildren's Hospital FoundationPublic Health Agency of Canada
KeywordsMedicineVaccinationPregnancyOdds ratioLogistic regressionOddsCoronavirus disease 2019 (COVID-19)ObstetricsPediatricsInternal medicineImmunologyDisease

Abstract

fetched live from OpenAlex

Abstract Background Pregnant individuals have been receiving COVID-19 vaccines following pre-authorization clinical trials in non-pregnant people. This study aimed to determine significant health events amongst pregnant females after COVID-19 vaccination, compared with unvaccinated pregnant controls and vaccinated non-pregnant individuals. Methods Study participants were pregnant and non-pregnant females aged 15-49 years who had received any COVID-19 vaccine, and pregnant unvaccinated controls. Participants reported significant health events occurring within seven days of vaccination. We employed multivariable logistic regression to examine significant health events associated with mRNA vaccines. Findings Overall 226/5,597(4.0%) vaccinated pregnant females reported a significant health event after dose one of an mRNA vaccine, and 227/3,108(7.3%) after dose two, compared with 11/339(3.2%) pregnant unvaccinated females. Pregnant vaccinated females had an increased odds of a significant health event after dose two of mRNA-1273 (aOR 4.4,95%CI 2.4-8.3) compared to pregnant unvaccinated controls, but not after dose one of mRNA-1273 or any dose of BNT162b2. Pregnant females had decreased odds of a significant health event compared to non-pregnant females after both dose one (aOR 0.63,95%CI 0.55-0.72) and dose two (aOR 0.62,95%CI 0.54-0.71) of mRNA vaccination. There were no significant differences in any analyses when restricted to events which led to medical attention. Interpretation COVID-19 mRNA vaccines have a good safety profile in pregnancy. Rates of significant health events were higher after dose two of mRNA-1273 compared with unvaccinated controls, with no difference when considering events leading to medical consultation. Rates of significant health events were lower in pregnant females than similarly aged non-pregnant individuals. Funding This work was supported by the COVID-19 Vaccine Readiness funding from the Canadian Institutes of Health Research and the Public Health Agency of Canada CANVAS grant number CVV-450980 and by funding from the Public Health Agency of Canada, through the Vaccine Surveillance Reference Group and the COVID-19 Immunity Task Force.

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)0.004
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 score0.028
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.044
GPT teacher head0.345
Teacher spread0.301 · 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".

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Citations2
Published2022
Admission routes3
Has abstractyes

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