Pre-conception blood pressure and evidence of placental malperfusion
Bibliographic record
Abstract
BACKGROUND: Evidence of placental maternal vascular malperfusion is associated with significant perinatal outcomes such as preeclampsia, intrauterine growth restriction and preterm birth. Elevations in pre-pregnancy blood pressure increase the risk for poor perinatal outcomes; however, the evidence linking pre-pregnancy blood pressure and placental malperfusion is sparse. MATERIALS AND METHODS: We conducted a retrospective case-control study of women with singleton gestations with placental evaluations who delivered at Magee-Womens Hospital in 2012. Charts from 100 deliveries with placental malperfusion lesions (vasculopathy, advanced villous maturation, infarct, or fibrin deposition) and 102 deliveries without placental malperfusion were randomly selected for screening. Blood pressure, demographic, and clinical data were abstracted from pre-pregnancy electronic medical records and compared between women with and without subsequent placental malperfusion lesions. RESULTS: Overall, 48% of women had pre-pregnancy records, and these were similarly available for women with and without placental malperfusion. Women with placental malperfusion demonstrated a reduction in their pre- to early pregnancy decrease in diastolic blood pressure (DBP). Adjusted for race, pre-pregnancy BMI, age, pre-conception interval, and gestational age at the first prenatal visit, the difference in pre- to early pregnancy DBP was significantly less in women with placental malperfusion compared to those without this pathologic finding (- 1.35 mmHg drop vs - 5.6mmg, p < 0.05). CONCLUSION: A blunted early gestation drop in DBP may be a risk factor for placental malperfusion, perhaps related to early pregnancy vascular maladaptation. The ability of the electronic medical record to provide pre-pregnancy data serves as an underutilized approach to study pre-pregnancy health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".