Vascular Function in Women and Children Following a Pregnancy Complicated by Preeclampsia
Bibliographic record
Abstract
Preeclampsia is a severe hypertensive complication of pregnancy that poses significant maternal and fetal risk. Evidence suggests that preeclampsia is linked to later-life cardiovascular disease development in both mother and offspring. However, the mechanisms responsible for this phenomenon have not been determined, nor how they can be ameliorated. Non-invasive vascular assessment allows for the evaluation of subclinical indicators of cardiovascular risk. Herein we describe a series of studies which were undertaken to measure postpartum and offspring vascular functional alterations associated with preeclampsia in pregnancy and to improve the accuracy and clinical applicability of laser perfusion imaging. Our studies of postpartum women reveal that those with prior severe preeclampsia display heightened microvascular endothelium-dependent and -independent vasodilation and exhibit higher carotid stiffness compared to women with mild or no disease. In a small trial of offspring born to preeclamptic pregnancies, microvascular function does not appear to differ from uncomplicated counterparts, though recovery of perfusion after occlusion appears to occur faster. Finally, we describe the novel application of a computer vision modality to enable frame-by-frame segmentation of participant movement during a trial. Importantly, we demonstrate that a computer vision modality makes similar predictions to an experienced human rater in a fraction of the time. In short, the present data add to the growing understanding of postpartum mechanisms associated with preeclampsia which may predispose women to future disease, and the broad applications of perfusion imaging as a tool for measurement and disease surveillance.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".