Arterial stiffness for the early prediction of pre‐eclampsia compared with blood pressure, uterine artery Doppler and angiogenic biomarkers: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Our aim was to evaluate the ability of arterial stiffness parameters to predict pre-eclampsia early compared with peripheral blood pressure, uterine artery Doppler and established angiogenic biomarkers. DESIGN: Prospective cohort study. SETTING: Tertiary care antenatal clinics in Montreal, Canada. POPULATION: Women with singleton high-risk pregnancies. METHODS: In the first trimester, arterial stiffness was measured by applanation tonometry, along with peripheral blood pressure and serum/plasma angiogenic biomarkers; uterine artery Doppler was measured in the second trimester. The predictive ability of different metrics was assessed through multivariate logistic regression. MAIN OUTCOME MEASURES: Arterial stiffness (carotid-femoral pulse wave velocity, carotid-radial pulse wave velocity) and wave reflection (augmentation index, reflected wave start time), peripheral blood pressure, ultrasound indices of velocimetry and circulating angiogenic biomarker concentrations. RESULTS: In this prospective study, among 191 high-risk pregnant women, 14 (7.3%) developed pre-eclampsia. A first-trimester 1 m/s increase in carotid-femoral pulse wave velocity was associated with 64% increased odds (P < 0.05), and a 1-millisecond increase in time to wave reflection with 11% decreased odds for pre-eclampsia (P < 0.01). The area under the curve of arterial stiffness, blood pressure, ultrasound indices and angiogenic biomarkers was 0.83 (95% confidence interval [CI] 0.74-0.92), 0.71 (95% CI 0.57-0.86), 0.58 (95% CI 0.39-0.77), and 0.64 (95% CI 0.44-0.83), respectively. With a 5% false-positive rate, blood pressure had a sensitivity of 14% for pre-eclampsia and arterial stiffness a sensitivity of 36%. CONCLUSIONS: Arterial stiffness predicted pre-eclampsia earlier and with greater ability than blood pressure, ultrasound indices or angiogenic biomarkers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".