Coronavirus disease 2019 among pregnant Chinese women: case series data on the safety of vaginal birth and breastfeeding
Bibliographic record
Abstract
OBJECTIVE: To assess whether vaginal secretions and breast milk of women with coronavirus disease 2019 (COVID-19) contain severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). DESIGN: Single centre cohort study. SETTING: Renmin Hospital of Wuhan University, Wuhan, Hubei province, China. POPULATION: We studied 13 SARS-CoV-2-infected pregnant women diagnosed between 31 January and 9 March 2020. METHODS: We collected clinical data, vaginal secretions, stool specimens and breast milk from SARS-CoV-2-infected women during different stages of pregnancy and collected neonatal throat and anal swabs. MAIN OUTCOMES AND MEASURES: We assessed viral presence in different biosamples. RESULTS: Of the 13 women with COVID-19, five were in their first trimester, three in their second trimester and five in their third trimester. Of the five women in their third trimester who gave birth, all delivered live newborns. Among these five deliveries, the primary adverse perinatal outcomes included premature delivery (n = 2) and neonatal pneumonia (n = 2). One of nine stool samples was positive; all 13 vaginal secretion samples, and five throat swabs and four anal swabs collected from neonates, were negative for the novel coronavirus. However, one of three samples of breast milk was positive by viral nucleic acid testing. CONCLUSIONS: In this case series of 13 pregnant women with COVID-19, we observed negative viral test results in vaginal secretion specimens, suggesting that a vaginal delivery may be a safe delivery option. However, additional research is urgently needed to examine breast milk and the potential risk for viral contamination. TWEETABLE ABSTRACT: New evidence for the safety of vaginal delivery and breastfeeding in pregnant women infected with SARS-CoV-2, positive viral result in a breast-milk sample.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".