Editorial: Maternal SARS-CoV-2 Infection and Pregnancy Outcomes from Current Global Study Data
Bibliographic record
Abstract
During the global COVID-19 pandemic, data from clinical studies, systematic review, and population registry data have shown that when compared with non-pregnant women, SARS-CoV-2 infection in pregnancy is associated with a small increase in risk to the mother. Large cohort studies and registry data collected from 2020 have included the US Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET), COVI-PREG, the UK and Global Pregnancy and Neonatal Outcomes in COVID-19 (PAN-COVID) study, the American Academy of Pediatrics (AAP) Section on Neonatal-Perinatal Medicine (SONPM) National Perinatal COVID-19 Registry, the Swedish Pregnancy Register, and the Canadian Surveillance of COVID-19 in Pregnancy (CANCOVID-Preg) registry. Recently published data have shown that most maternal infections with SARS-CoV-2 occur during the third trimester and result in a small increase in hospital admission, admission to the intensive care unit (ICU), mechanical ventilation, preterm birth, and increased cesarean sections in mothers infected with SARS-CoV-2. However, currently approved vaccines given in pregnancy result in an immune response to current SARS-CoV-2 variants. Transplacental transmission of SARS-CoV-2 to the fetus can occur, but the immediate and long-term effects on the newborn infant remain unclear. Therefore, women who are pregnant or planning a pregnancy should be managed according to current clinical guidelines with timely vaccination to prevent infection with SARS-CoV-2. This Editorial summarizes what is currently known about maternal SARS-CoV-2 infection and pregnancy outcomes from multinational studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".