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Record W4383874577 · doi:10.1136/bmjmed-2023-000632

Pregnancy, fetal, and neonatal outcomes after a first booster dose of covid-19 vaccine during pregnancy in Ontario, Canada: population based, retrospective cohort study

2023· article· en· W4383874577 on OpenAlexafffundabout
Deshayne B. Fell, Sheryll Dimanlig-Cruz, Eszter Török, Siri E. Håberg, Annette K. Regan, Jay S. Kaufman, Robert W. Platt, Christopher A. Gravel, Liam Bruce, Prakesh S. Shah, Kumanan Wilson, Ann E. Sprague, Gillian D. Alton, Tavleen Dhinsa, Darine El‐Chaâr, Sarah A. Buchan, Jeffrey C. Kwong, Sarah E. Wilson, Sandra Dunn, Shannon E. MacDonald, Jon Barrett, Nannette Okun, Mark Walker

Bibliographic record

VenueBMJ Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsUniversity of AlbertaPublic Health OntarioBruyèreUniversity of TorontoUniversity of CalgaryMount Sinai HospitalUniversity of OttawaMcGill UniversityInstitute for Clinical Evaluative SciencesChildren's Hospital of Eastern OntarioMcMaster UniversityOntario Stroke NetworkOttawa Hospital
FundersDepartment of Family and Community Medicine, University of TorontoNorges ForskningsrådUniversity of TorontoNordForskPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicinePregnancyPlacental abruptionObstetricsPopulationHazard ratioChorioamnionitisRetrospective cohort studyGestational agePediatricsConfidence intervalFetusInternal medicine

Abstract

fetched live from OpenAlex

Objective: To assess risk of adverse pregnancy, fetal, and neonatal outcomes after a third dose (first booster dose) of covid-19 vaccine during pregnancy among individuals who had completed both doses of primary covid-19 vaccine series before pregnancy. Design: Population based, retrospective cohort study. Setting: Ontario, Canada, from 20 December 2021 to 31 August 2022. Participants: Individuals were included if they were pregnant with an expected date of delivery from 20 December 2021 (start date of third dose eligibility for everyone ≥18 years) to 31 August 2022, who had completed the two doses of primary covid-19 messenger RNA vaccine series before pregnancy, and became eligible for a third dose (≥six months since dose two) before the end of pregnancy. Main outcome measures: Pregnancy outcomes included hypertensive disorders of pregnancy, placental abruption, caesarean delivery, chorioamnionitis, and postpartum hemorrhage. Fetal and neonatal outcomes included stillbirth, preterm birth, admission to neonatal intensive care unit for >24 h, newborn 5 min Apgar score <7, and small-for-gestational age infant (<10th percentile). We estimated hazard ratios and 95% confidence intervals for study outcomes, treating dose three as a time varying exposure and adjusting for confounding using inverse probability weighting. Results: Among 32 689 births, 18 491 (56.6%) were born to individuals who received a third covid-19 dose during pregnancy. Compared with eligible individuals who did not receive a third dose during pregnancy, no increased risks were associated with receiving a third covid-19 vaccine dose during pregnancy for placental abruption (adjusted hazard ratio 0.84 (95% confidence interval 0.70 to 1.02)), chorioamnionitis (0.67 (0.49 to 0.90)), postpartum haemorrhage (1.01 (0.89 to 1.16)), caesarean delivery (0.90 (0.87 to 0.94)), stillbirth (0.56 (0.39 to 0.81)), preterm birth (0.91 (0.84 to 0.99)), neonatal intensive care unit admission (0.96 (0.90 to 1.03)), 5 min Apgar score<7 (0.96 (0.82 to 1.14)), or small-for-gestational age infant (0.86 (0.79 to 0.93)). Conclusion: Receipt of a third covid-19 vaccine dose during pregnancy was not associated with an increased risk of adverse pregnancy, fetal, or neonatal outcomes. These findings can help to inform evidence based decision making about the risks and benefits of covid-19 booster doses during pregnancy.

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)0.001
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 score0.035
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.026
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

Citations6
Published2023
Admission routes3
Has abstractyes

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