Antenatal and perinatal factors influencing neonatal blood pressure: a systematic review
Bibliographic record
Abstract
OBJECTIVE: A comprehensive understanding of the factors contributing to perinatal blood pressure is vital to ensure optimal postnatal hemodynamic support. The objective of this study was to review existing literature on maternal and perinatal factors influencing blood pressure in neonates up to 3 months corrected age. METHODS: A systematic search of published literature in OVID Medline, OVID Embase and the COCHRANE library identified publications relating to maternal factors affecting blood pressure of neonates up to corrected age of 3 months. Summary data were extracted and compared (PROSPERO CRD42018092886). RESULTS: Of the 3683 non-duplicate publications identified, 44 were eligible for inclusion in this review. Topics elicited were sociodemographic factors, maternal health status, medications, smoking during pregnancy, and cord management at birth. Limited data were available for each factor. Results regarding the impact of these factors on neonatal blood pressure were inconsistent across studies. CONCLUSIONS: There is insufficient evidence to draw definitive conclusions regarding the impact of various maternal and perinatal factors on neonatal blood pressure. Future investigations of neonatal cardiovascular therapies should account for these factors in their study design. Similarly, studies on maternal diseases and perinatal interventions should include neonatal blood pressure as part of their primary or secondary analyses.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".