COVID-19 and pregnancy: a review of current knowledge.
Bibliographic record
Abstract
BACKGROUND: Since December 2019, coronavirus disease 2019 (COVID-19) has become a major health problem that is spreading all over the world. Several viral infections such as SARS, MERS, and influenza have been associated with adverse pregnancy outcomes. The question arises whether pregnant women are at greater risk of complications related to COVID-19 compared to other people What complications should we expect in the fetuses whose mothers were infected? AIMS: This review aims to provide a summary of studies on symptoms of COVID-19 and the possible risks of COVID-19 among pregnant women, as well as complications in fetuses and neonates whose mothers were infected with COVID-19. METHODS: The included data were provided from Web of Science, Cochrane, PubMed, and Scopus which are extracted from the published studies in English until April 2nd, 2020 that contained data on the risk of COVID-19 in pregnancy. RESULTS: The early symptoms of patients with COVID-19 were fever, cough, dyspnea, myalgia, and fatigue; while production of sputum, headache, hemoptysis, and diarrhea were other symptoms which were less common. There is no evidence of vertical maternal-fetal transmission in pregnant women with COVID-19. CONCLUSIONS: The clinical findings in pregnant women with COVID-19 are not significantly different compared to other patients, and pregnant women with COVID-19 are not at a higher risk of developing critical pneumonia compared to non-pregnant women. Although, there has been no sign of vertical infection in infants, but maternal infection can cause serious problems such as preterm labour and fetal distress.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".