Bibliographic record
Abstract
The blood pressure (BP) of neonates, especially those born premature, changes rapidly over the first days and weeks of life. Neonatal BPs may be affected by maternal factors, perinatal factors or events, and intrinsic or extrinsic infant factors. Unfortunately, the effect of many maternal health and disease states has only been studied in small numbers or has shown conflicting results. Many events around the time of delivery have the potential to influence the neonatal BP, and while definitive studies are often lacking, some observational data support physiological expectations. The strongest determinants of neonatal BP are the infant factors of gestational age at birth, birth weight, and postmenstrual age. Understanding the expected pattern of BP changes, identifying the potential influencing factors, and accurately measuring the BP are all essential to determine whether there is a BP abnormality present but are also more complex in the neonatal population. This review describes the evidence for maternal conditions, perinatal events, and infant factors to affect neonatal BP. It also explains what is currently known about the changing BP patterns in neonates including those born preterm. In addition, by examining the physiological process of hemodynamic adaptation to the extrauterine environment and compensatory cardiovascular responses, we can gain insight into the expected and unexpected vascular responses, making the variability of neonatal BP seem a little more predictable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".