The impact of COVID-19 on pregnancy outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The impact of coronavirus disease 2019 (COVID-19) on maternal and newborn health is unclear. We aimed to evaluate the association between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during pregnancy and adverse pregnancy outcomes. METHODS: We conducted a systematic review and meta-analysis of observational studies with comparison data on SARS-CoV-2 infection and severity of COVID-19 during pregnancy. We searched for eligible studies in MEDLINE, Embase, ClinicalTrials.gov, medRxiv and Cochrane databases up to Jan. 29, 2021, using Medical Subject Headings terms and keywords for "severe acute respiratory syndrome coronavirus 2 OR SARS-CoV-2 OR coronavirus disease 2019 OR COVID-19" AND "pregnancy." We evaluated the methodologic quality of all included studies using the Newcastle-Ottawa Scale. Our primary outcomes were preeclampsia and preterm birth. Secondary outcomes included stillbirth, gestational diabetes and other pregnancy outcomes. We calculated summary odds ratios (ORs) or weighted mean differences with 95% confidence intervals (CI) using random-effects meta-analysis. RESULTS: We included 42 studies involving 438 548 people who were pregnant. Compared with no SARS-CoV-2 infection in pregnancy, COVID-19 was associated with preeclampsia (OR 1.33, 95% CI 1.03 to 1.73), preterm birth (OR 1.82, 95% CI 1.38 to 2.39) and stillbirth (OR 2.11, 95% CI 1.14 to 3.90). Compared with mild COVID-19, severe COVID-19 was strongly associated with preeclampsia (OR 4.16, 95% CI 1.55 to 11.15), preterm birth (OR 4.29, 95% CI 2.41 to 7.63), gestational diabetes (OR 1.99, 95% CI 1.09 to 3.64) and low birth weight (OR 1.89, 95% CI 1.14 to 3.12). INTERPRETATION: COVID-19 may be associated with increased risks of preeclampsia, preterm birth and other adverse pregnancy outcomes.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.045 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".