SARS-CoV-2 and the role of vertical transmission from infected pregnant women to their fetuses: systematic review
Bibliographic record
Abstract
Background Vertical transmission of SARS-CoV-2 has been reported but appears uncommon. Objectives This study systematically reviewed the evidence on vertical transmission of SARS-CoV-2 from pregnant women to their neonates. Search strategy Literature searches in WHO Covid-19 Database, LitCovid, medRxiv, and Google Scholar for SARS-CoV-2 using keywords and associated synonyms, search date to 20 December 2020; no language restrictions. Selection criteria Studies of any design reporting transmission. Data collection and analysis Two reviewers independently assessed article eligibility and extracted data. Results were reported descriptively; no meta-analyses were possible. Main results 106 studies were included: 40 reviews and 66 primary studies, most conducted in hospitals. 32 case reports were assessed as high risk of bias, due to the study design; across the 34 remaining primary studies, risk of bias was low to moderate. Sixteen case reports described vertical transmission. In cohort studies and case series, 65/2391 (2.7%) neonates born to mothers with a COVID-19 diagnosis tested positive for SARS-CoV-2 within 24 hours of birth; the proportion of positive neonates ranged from 0% to 22%. Twenty studies reported no vertical transmission. Maternal symptomatology and mode of delivery were not correlated with vertical transmission. 7/25 studies of placental tissue identified SARS-CoV-2; vertical transmission was infrequent. No study reported the results of viral culture to detect SARS-CoV-2. Conclusions These findings indicate that vertical transmission is possible, but not frequent. Further high-quality studies are needed to understand vertical transmission. Funding World Health Organization: WHO registration No 2020/1077093.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".