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Determinants of Blood Pressure in Neonates and Infants

2021· review· en· W3127126058 on OpenAlexaff
Janis M. Dionne

Bibliographic record

VenueHypertension · 2021
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineBlood pressurePopulationGestational ageObservational studyBirth weightDiseasePediatricsAbnormalityHemodynamicsPregnancyInternal medicineBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.945
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.336
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations23
Published2021
Admission routes1
Has abstractyes

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