Risk of Severe Maternal Morbidity Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection During Pregnancy
Bibliographic record
Abstract
Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during pregnancy increases the risk of adverse fetal and neonatal outcomes, but the contribution to severe maternal morbidity (SMM) has been less frequently documented. Methods We conducted a national cohort study of 93 624 deliveries occurring between 11 March 2020 and 1 July 2021 using medical claims information from the OptumLabs Data Warehouse. SARS-CoV-2 infection was identified from diagnostic and laboratory testing claims records. We identified 21 SMM conditions using International Classification of Diseases, Tenth Revision, Clinical Modification and procedure codes and compared SMM conditions by SARS-CoV-2 status using Poisson regression with robust variance, adjusting for maternal sociodemographic and health factors, onset of labor, and week of conception. Results Approximately 5% of deliveries had a record of SARS-CoV-2 infection: 27.0% <7 days before delivery, 13.5% within 7–30 days of delivery, and 59.5% earlier in pregnancy. Compared to uninfected pregnancies, the adjusted risk of SMM was 2.22 times higher (95% confidence interval [CI], 1.97–2.48) among those infected <7 days before delivery and 1.66 times higher (95% CI, 1.23–2.08) among those infected 7–30 days before delivery. The highest risks were observed for acute respiratory distress syndrome (adjusted risk ratio [aRR], 13.24 [95% CI, 12.86–13.61]) and acute renal failure (aRR, 3.91 [95% CI, 3.32–4.50]). Conclusions COVID-19 is associated with increased rates of SMM.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".