Clinical Manifestation and Maternal Complications and Neonatal outcomes in Pregnant Women with COVID 19: An Update a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Existing evidence indicates that the risk of obstetric and perinatal outcomes is higher in women with coronavirus infection. outbreaks suggest that pregnant women and their fetuses are particularly susceptible to poor outcomes. However, there is little known about pregnancy related complications and co-morbidity in this group of women. Therefore, this, systematic review and meta-analysis performed in order to find out whether COVID-19 may cause different manifestations and outcomes in antepartum and postpartum period or not.Methods We searched databases, including Medline (PubMed), Embase, Scopus, Web of sciences, Cochrane library, Ovid and CINHAL to retrieve all articles reporting the prevalence of maternal and neonatal complications, in addition clinical manifestations, in pregnant women with COVID 19 that published with English language from January to April 2020. Results 11 studies with total 177 pregnant women included in this systematic review.Results show that the pooled prevalence of neonatal mortality, lower birth weight, stillbirth, premature birth, and intrauterine fetal distress in women with COVID 19 were 4% (95% Cl: 1 - 9%), 21% (95% Cl: 11 – 31%), 2% (95% Cl: 1 - 6%), 28% (95% Cl: 12 - 44%), and 15% (95% Cl: 4 - 26%); respectively. Also the pooled prevalence of fever, cough, diarrhea and dyspnea were 56% (95% Cl: 30 - 83%), 30% (95% Cl: 21 - 39%), 9% (95% Cl: 2 - 16%), and 3% (95% Cl: 1 - 6%) in the pregnant women with COVID-19.Conclusion According to this systematic review and meta-analysis, the pregnant women with COVID-19 with or without pneumonia, are at a higher risk of pre-eclampsia, preterm birth, miscarriage and cesarean delivery. Furthermore, the risk of LBW and intrauterine fetal distress seems increased in neonates.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".