SARS-CoV-2 Infection During Pregnancy and Associated Perinatal Health Outcomes: A National US Cohort Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it