Proteinuria and venous thromboembolism in pregnancy: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Pregnancy-associated venous thromboembolism (VTE) is associated with high morbidity and mortality. Identification of risk factors of VTE may lead to improved maternal and foetal outcomes. Proteinuria confers a pro-thrombotic state, however, its association with VTE in pregnancy remains unknown. We set out to assess the association of proteinuria and VTE during pregnancy. METHODS: = 306 244; mean age 29.8 years) from Ontario, Canada. Proteinuria was defined by any of the following: urine albumin:creatinine ratio ≥3 mg/mmol, urine protein:creatinine ratio ≥5 mg/mmol or urine dipstick proteinuria ≥1. The main outcome measure was a diagnosis of VTE up to 24-weeks post-partum. RESULTS: A positive proteinuria measurement occurred in 8508 (2.78%) women and was more common with a history of kidney disease, gestational or non-gestational diabetes mellitus and hypertension. VTE events occurred in 625 (0.20%) individuals, with a higher risk among women with positive proteinuria [32 events (0.38%)] compared with women without proteinuria [593 events (0.20%); inverse probability-weighted risk ratio 1.79 (95% confidence interval 1.25-2.57)]. The association was consistent using a more specific VTE definition, in the post-partum period, in high-risk subgroups (hypertension or diabetes) and when the sample was restricted to women with preserved kidney function. CONCLUSIONS: The presence of proteinuria in the first 20 weeks of pregnancy is associated with a significantly higher risk of VTE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".