COVID-19 and pregnancy: epidemiology, clinical features, maternal and perinatal outcomes. A systematic review
Bibliographic record
Abstract
Background and objectives: COVID-19 pandemic had quite a significant impact on a number of obstetric outcomes. This is often directly attributed to complications of COVID-19. This article is a systematically review literature on the epidemiology, clinical features, maternal and perinatal outcomes of COVID-19 in pregnancy.Materials and methods. A PRISMA methodology search was conducted on the databases of PubMed, Scopus, Medline, Google Scholar, Web of Science and Central BMJ using MeSH keywords or combinations of the words “COVID-19”, “SARS-CoV-2”, “pregnancy”, “epidemiology”, “comorbid disease”, “pregnancy and childbirth outcome”, “preeclampsia”, “fetus”. Only articles published between December 1, 2019 to February 28, 2022 were considered. After preliminary analysis of more than 600 publications, 21 articles were short-listed for final processing. The studies were selected using a Newcastle-Ottawa scale style questionnaire. The clinical features, risk factors, co-morbid conditions, maternal and neonatal outcomes were presented in two separate tables respectively. Results. COVID-19 incidence in pregnancy ranged from 4.9% to 10.0%. Such women were 5.4 times more likely to be hospitalized and 1.5 times more to need ICU care. Dyspnoea and hyperthermia were associated with a high risk of severe maternal (OR 2.56; 95% CI 1.92–3.40) and neonatal complications (OR 4.97; 95% CI 2.11–11.69). One in ten of neonates had a small weight for gestational age (9.27 ± 3.18%) and one in three required intensive care unit observation.Conclusions. Despite the increasingly emerging evidence on the associations between pregnancy and COVID-19 infection, the data is sometimes contradictory necessitating further 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".