Impact of Covid-19 on risk of severe maternal morbidity
Bibliographic record
Abstract
BACKGROUND: We examined the risk of severe life-threatening morbidity in pregnant patients with Covid-19 infection. METHODS: We conducted a population-based study of 162,576 pregnancies between March 2020 and March 2022 in Quebec, Canada. The main exposure was Covid-19 infection, including the severity, period of infection (antepartum, peripartum), and circulating variant (wildtype, alpha, delta, omicron). The outcome was severe maternal morbidity during pregnancy up to 42 days postpartum. We estimated risk ratios (RR) and 95% confidence intervals (CI) for the association between Covid-19 infection and severe maternal morbidity using adjusted log-binomial regression models. RESULTS: Covid-19 infection was associated with twice the risk of severe maternal morbidity compared with no infection (RR 2.02, 95% CI 1.76-2.31). Risks were elevated for acute renal failure (RR 3.01, 95% CI 1.79-5.06), embolism, shock, sepsis, and disseminated intravascular coagulation (RR 1.35, 95% CI 0.95-1.93), and severe hemorrhage (RR 1.49, 95% CI 1.09-2.04). Severe antepartum (RR 13.60, 95% CI 10.72-17.26) and peripartum infections (RR 20.93, 95% CI 17.11-25.60) were strongly associated with severe maternal morbidity. Mild antepartum infections also increased the risk, but to a lesser magnitude (RR 3.43, 95% CI 2.42-4.86). Risk of severe maternal morbidity was around 3 times greater during circulation of wildtype and the alpha and delta variants, but only 1.2 times greater during omicron. CONCLUSIONS: Covid-19 infection during pregnancy increases risk of life-threatening maternal morbidity, including renal, embolic, and hemorrhagic complications. Severe Covid-19 infection with any variant in the antepartum or peripartum periods all increase the risk of severe maternal morbidity.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".