COVID-19 during pregnancy: an overview of maternal characteristics, clinical symptoms, maternal and neonatal outcomes of 10,996 cases described in 15 countries
Bibliographic record
Abstract
The objective of this review was to identify the most significant studies reporting on COVID-19 during pregnancy and to provide an overview of SARS-CoV-2 infection in pregnant women and perinatal outcomes. Eligibility criteria included all reports, reviews; case series with more than 100 individuals and that reported at least three of the following: maternal characteristics, maternal COVID-19 clinical presentation, pregnancy outcomes, maternal outcomes and/or neonatal/perinatal outcomes. We included eight studies that met the inclusion criteria, representing 10,966 cases distributed in 15 countries around the world until July 20, 2020. The results of our review demonstrate that the maternal characteristics, clinical symptoms, maternal and neonatal outcomes almost 11,000 cases of COVID-19 and pregnancy described in 15 different countries are not worse or different from the general population. We suggest that pregnant women are not more affected by the respiratory complications of COVID-19, when compared to the outcomes described in the general population. We also suggest that the important gestational shift Th1-Th2 immune response, known as a potential contributor to the severity in cases of viral infections during pregnancy, are counter-regulated by the enhanced-pregnancy-induced ACE2-Ang-(1-7) axis. Moreover, the relatively small number of reported cases during pregnancy does not allow us to affirm that COVID-19 is more aggressive during pregnancy. Conversely, we also suggest, that down-regulation of ACE2 receptors induced by SARS-CoV-2 cell entry might have been detrimental in subjects with pre-existing ACE2 deficiency associated with pregnancy. This association might explain the worse perinatal outcomes described in the literature.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".