The association between pulse wave velocity and pregnancy-associated diseases: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Maintaining healthy vascular structure and function is important for a healthy pregnancy. Obesity is a well-known predictor for poor postoperative outcomes of vascular surgery. However, the association between pulse wave velocity (PWV), a well-recognized parameter for arterial stiffness assessment, and pregnancy-associated diseases is still unclear. Therefore, we conducted this systematic review, and a meta-analysis was performed to assess the relevant associations. Methods: We systematically searched the Web of Science and PubMed databases to obtain articles on PWV and pregnancy-associated diseases published before April 2023. The mean with standard deviation was used to assess the differences in PWV in pregnant women with or without relevant diseases. Subgroup analysis was conducted according to specific types of PWV. The Newcastle‒Ottawa Scale was used to evaluate the quality of the enrolled studies. Results: A total of 6488 individuals from 21 studies were included. All enrolled studies were high-quality. Overall, the PWV was elevated in pregnant women who suffered from preeclampsia (mean difference (MD) = 0.67, 95 % confidence interval (CI): 0.51,0.83, P < 0.00001), hypertension (MD = 1.04, 95 % CI: 1.00,1.08, P < 0.00001), gestational diabetes mellitus (MD = 0.34, 95%CI: 0.19,0.48, P < 0.00001), and diabetes (MD = 0.49, 95%CI: 0.27,0.70, P < 0.00001). Subgroup analysis based on specific types of PWV showed similar results. Conclusion: In our study, PWV is elevated in pregnancy-associated diseases, including preeclampsia, hypertension, and diabetes. The PWV assessment should be regarded as a clinical routine for pregnant women to prevent and manage cardiovascular diseases during pregnancy.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".