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Record W4316928669 · doi:10.1136/bmjgh-2022-009495

Adverse maternal, fetal, and newborn outcomes among pregnant women with SARS-CoV-2 infection: an individual participant data meta-analysis

2023· review· en· W4316928669 on OpenAlexaffabout
Emily R. Smith, Erin Oakley, Gargi Wable Grandner, Kacey Ferguson, Fouzia Farooq, Yalda Afshar, Mia Ahlberg, Homa K. Ahmadzia, Victor Akelo, Grace M. Aldrovandi, Beth A. Tippett Barr, Elisa Bevilacqua, Justin S. Brandt, Nathalie Broutet, Irene Fernández‐Buhigas, J. Carrillo, Rebecca G. Clifton, Jeanne A. Conry, Erich Cosmi, F. Crispi, F. Crovetto, Camille Delgado‐López, Hema Divakar, Amanda J. Driscoll, Guillaume Favre, Valerie J. Flaherman, Chris Gale, Sami L. Gottlieb, E. Gratacós, Olivia Allende Hernández, Stephanie Jones, Erkan Kalafat, Sammy Khagayi, Marian Knight, Karen L. Kotloff, Antonio L’Abbate, Kirsty Le Doaré, C. Lees, Ethan Litman, Erica M. Lokken, Valentina Laurita Longo, Shabir A. Madhi, Laura A. Magee, R.J. Martinez‐Portilla, Elizabeth M. McClure, Tori D Metz, Emily S. Miller, Deborah Money, Sakita Moungmaithong, Edward Mullins, Jean B. Nachega, Marta C. Nunes, Dickens Onyango, Alice Panchaud, Liona C. Poon, Daniel J. Raiten, Lesley Regan, Gordon Rukundo, Daljit Singh Sahota, Allie Sakowicz, José Enrique Sanín-Blair, Jonas Söderling, Olof Stephansson, Marleen Temmerman, Anna Thorson, Jorge E. Tolosa, Julia Townson, Miguel Valencia‐Prado, Silvia Visentin, Peter von Dadelszen, Kristina M. Adams Waldorf, Clare Whitehead, Murat Yassa, Jim M Tielsch

Bibliographic record

VenueBMJ Global Health · 2023
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentKoç ÜniversitesiWorld Health OrganizationRobert Wood Johnson Medical School, Rutgers, The State University of New JerseyNordForskNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates Foundation
KeywordsMeta-analysisMedicinePregnancyObstetricsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)PandemicFetusInternal medicineDiseaseInfectious disease (medical specialty)Biology

Abstract

fetched live from OpenAlex

INTRODUCTION: Despite a growing body of research on the risks of SARS-CoV-2 infection during pregnancy, there is continued controversy given heterogeneity in the quality and design of published studies. METHODS: We screened ongoing studies in our sequential, prospective meta-analysis. We pooled individual participant data to estimate the absolute and relative risk (RR) of adverse outcomes among pregnant women with SARS-CoV-2 infection, compared with confirmed negative pregnancies. We evaluated the risk of bias using a modified Newcastle-Ottawa Scale. RESULTS: We screened 137 studies and included 12 studies in 12 countries involving 13 136 pregnant women.Pregnant women with SARS-CoV-2 infection-as compared with uninfected pregnant women-were at significantly increased risk of maternal mortality (10 studies; n=1490; RR 7.68, 95% CI 1.70 to 34.61); admission to intensive care unit (8 studies; n=6660; RR 3.81, 95% CI 2.03 to 7.17); receiving mechanical ventilation (7 studies; n=4887; RR 15.23, 95% CI 4.32 to 53.71); receiving any critical care (7 studies; n=4735; RR 5.48, 95% CI 2.57 to 11.72); and being diagnosed with pneumonia (6 studies; n=4573; RR 23.46, 95% CI 3.03 to 181.39) and thromboembolic disease (8 studies; n=5146; RR 5.50, 95% CI 1.12 to 27.12).Neonates born to women with SARS-CoV-2 infection were more likely to be admitted to a neonatal care unit after birth (7 studies; n=7637; RR 1.86, 95% CI 1.12 to 3.08); be born preterm (7 studies; n=6233; RR 1.71, 95% CI 1.28 to 2.29) or moderately preterm (7 studies; n=6071; RR 2.92, 95% CI 1.88 to 4.54); and to be born low birth weight (12 studies; n=11 930; RR 1.19, 95% CI 1.02 to 1.40). Infection was not linked to stillbirth. Studies were generally at low or moderate risk of bias. CONCLUSIONS: This analysis indicates that SARS-CoV-2 infection at any time during pregnancy increases the risk of maternal death, severe maternal morbidities and neonatal morbidity, but not stillbirth or intrauterine growth restriction. As more data become available, we will update these findings per the published protocol.

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)0.030
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.030
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.057
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0200.072
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.534
GPT teacher head0.552
Teacher spread0.018 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations180
Published2023
Admission routes2
Has abstractyes

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