SARS-CoV-2 Infection During Pregnancy and Associated Perinatal Health Outcomes: A National US Cohort Study
Bibliographic record
Abstract
BACKGROUND: Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been associated with increased risk of adverse perinatal health outcomes, few large-scale, community-based epidemiological studies have been conducted. METHODS: We conducted a national cohort study using deidentified administrative claims data for 78 283 pregnancies with estimated conception before 30 April 2020 and pregnancy end after 11 March 2020. We identified SARS-CoV-2 infections using diagnostic and laboratory testing data, and compared the risk of pregnancy outcomes using Cox proportional hazard models treating coronavirus disease 2019 (COVID-19) as a time-varying exposure and adjusting for baseline covariates. RESULTS: Of the pregnancies, 2655 (3.4%) had a documented SARS-CoV-2 infection. COVID-19 during pregnancy was not associated with risk of miscarriage, antepartum hemorrhage, or stillbirth, but was associated with 2-3 fold higher risk of induced abortion (adjusted hazard ratio [aHR], 2.60; 95% confidence interval [CI], 1.17-5.78), cesarean delivery (aHR, 1.99; 95% CI, 1.71-2.31), clinician-initiated preterm birth (aHR, 2.88; 95% CI, 1.93-4.30), spontaneous preterm birth (aHR, 1.79; 95% CI, 1.37-2.34), and fetal growth restriction (aHR, 2.04; 95% CI, 1.72-2.43). CONCLUSIONS: Prenatal SARS-CoV-2 infection was associated with increased risk of adverse pregnancy outcomes. Prevention could have fetal health benefits.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".