Indicators of Cardiometabolic Function in Pregnancy and Long-Term Risk of COVID-19: Population-Based Cohort Study
Bibliographic record
Abstract
Background: Pregnancy increases a woman’s susceptibility to severe COVID-19, especially those with metabolic dysfunction. It is unknown if markers of metabolic dysfunction commonly assessed around pregnancy are associated with COVID-19 illness after pregnancy. Aim: The aim of this study is to evaluate the indicators of metabolic dysfunction collected in pregnancy and the future risk of severe COVID-19 after pregnancy. Methods: This population-based cohort study was completed in all of Ontario, comprising 417,713 women aged 15-49 years with a hospital birth between April 2007 and March 2018. The main exposure was each 1-kg/m2 higher body mass index (BMI), 1-mmol/L higher glucose concentration at the 50-g glucose challenge test, and one-week earlier gestational week at delivery. The main outcome was severe COVID-19 illness or death, from the start of the pandemic period on March 1, 2020, till December 31, 2021. Results: The adjusted hazard ratio (aHR) of COVID-19 illness increased per 1-kg/m2 higher BMI (1.05, 95% CI 1.04-1.06), per 1-mmol/L higher serum glucose concentration (1.16, 95% CI 1.10-1.22), and for each one-week earlier gestational week at delivery (1.12, 95% CI 1.03-1.23). Relative to women with no dichotomized risk factors, the aHR for severe COVID-19 was 1.60 (95% CI 1.28-2.01) with one factor, 3.34 (95% CI 2.51-4.44) with two factors, and 4.52 (95% CI 2.11-9.67) with three factors. Conclusions: The number, and degree, of standard metabolic indicators measured around pregnancy predict the future risk of severe COVID-19 remotely after that pregnancy.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".