Blood Pressure Variability and Adverse Pregnancy and Cardiovascular Outcomes in the ALSPAC Cohort
Bibliographic record
Abstract
BACKGROUND: Outside pregnancy, blood pressure variability (BPV) predicts cardiovascular events. We aimed to study associations (if any) between visit-to-visit BPV in pregnancy and (1) adverse maternal/perinatal outcomes, and (2) long-term maternal cardiovascular outcomes. We conducted a secondary analysis of data from ALSPAC (Avon Longitudinal Study of Parents and Children). METHODS: Adjusted logistic regression assessed relationships between visit-to-visit BPV (by the measures of SD, average real variability, and variability independent of mean) and pregnancy outcomes (gestational/severe hypertension, preeclampsia, preterm birth, small-for-gestational-age infants, neonatal intensive care unit admission, stillbirth, and perinatal death). Adjusted Cox regression assessed relationships between visit-to-visit BPV measures and long-term maternal outcomes: hypertension (measured), diabetes (self-reported), and heart disease (self-reported) as a composite. RESULTS: Among 12 509 women in ALSPAC, 4956 answered a follow-up questionnaire and 4426 attended a follow-up clinic, an average of 22 years after the index pregnancy. Measures of variability in systolic and diastolic BP (by each of SD, average real variability, and variability independent of mean) were associated with adverse pregnancy outcomes, particularly severe hypertension and preeclampsia by SD and variability independent of mean (adjusted odds ratios, 1.30-2.11). BPV in pregnancy was not associated with hypertension, diabetes, or heart disease at follow-up in adjusted analyses. CONCLUSIONS: Our findings indicate that BP variation between antenatal visits is informative for identifying risk of short-term adverse pregnancy outcomes, but BPV provides no long-term utility in predicting cardiovascular risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".