Blunted Overnight Blood Pressure Dipping in Second Trimester; A Strong Predictor of Gestational Hypertension and Preeclampsia
Bibliographic record
Abstract
BACKGROUND: Preeclampsia is a global burden with 10 million incidences annually and 210 daily deaths worldwide. Diagnosis is mainly based on the features following full presentation. OBJECTIVE: This study explored whether early pregnancy circadian changes of ambulatory blood pressure monitoring (ABPM) could predict preeclampsia and hypertension. METHODS: In a prospective study, 294 pregnant women who were referred to Sarem Women's Hospital, Iran were recruited. Systolic, diastolic and mean arterial pressures (MAP) were recorded (diurnally and nocturnally) in each trimester. Dipping was defined as a minimum 10% decrease in blood pressure. RESULTS: Of the 251 women who completed the study, 25 percent (n=63) experienced blunted MAP dipping during sleep phases in the second trimester. Eighty-nine percent (n=56) experienced hypertensive disorder in the third trimester, one-third of which experienced preeclampsia. Of the women with normal MAP dipping (n=188), 5 percent (n=10) had gestational hypertension and 1 percent (n=2) became preeclamptic. (P<0.0001). CONCLUSION: This study clearly demonstrated blunted blood pressure dipping overnight during the second trimester which is a strong predictor of forthcoming pregnancy-induced hypertension and preeclampsia. A scoring system was developed to predict hypertensive disorder and it was significantly correlated with preeclampsia occurrence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".