Phenotypes of Pregnant Women Who Subsequently Develop Hypertension in Pregnancy
Bibliographic record
Abstract
Background Hypertensive disorders complicating pregnancy are a major cause of maternal death. Our objective was to evaluate maternal clinical, hemodynamic, and placental prognostic indicators in a consolidated manner to identify women who develop hypertension in pregnancy. Methods and Results Twenty‐six normotensive pregnant women from a specialized Placenta Clinic at increased risk of developing de novo hypertension and 20 normotensive healthy pregnant controls were recruited at 22 to 26 weeks' gestation. Fourteen maternal clinical, hemodynamic, and placental characteristics were assessed in the second trimester and aggregated. Principal component analysis of this combined data set determined that 3 dimensions accounted for 56% of the cohort variability. The first dimension accounted for 31% of the cohort variability, with significant contributions from total peripheral resistance, endoglin, and cardiac output. The second dimension was predominantly influenced by body mass index and mean arterial pressure, while uric acid and myeloperoxidase mainly contributed to the third dimension. Unsupervised clustering identified 3 groups within this combined data set. Total peripheral resistance was the most significant distinguishing parameter between these groups ( P <0.0001), followed by placental growth factor, endoglin, and cardiac output ( P <0.0001). Using these 4 parameters, a receiver operating curve was constructed with an area under the curve of 0.975 (95% confidence interval 0.93–1) for the prediction of developing hypertension in pregnancy. Conclusions Consolidated assessment of prognostic indicators in the second trimester of pregnancy may be useful to characterize and distinguish pathways by which women may develop hypertension in pregnancy. This approach could contribute to the development of pathway‐specific preventative and antihypertensive treatment strategies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".