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Record W2584782490 · doi:10.1097/fjc.0000000000000468

Mechanisms of Uterine Artery Dysfunction in Pregnancy Complications

2017· article· en· W2584782490 on OpenAlexaff
Jude S. Morton, Alison S. Care, Sandra T. Davidge

Bibliographic record

VenueJournal of Cardiovascular Pharmacology · 2017
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsMedicinePregnancyUterine arteryFetusPopulationObstetricsGestationBiology

Abstract

fetched live from OpenAlex

Pregnancy is a unique condition, and the vascular processes that are required for this undertaking are both complex and extensive. In this review, we discuss the vascular adaptations which occur in the maternal uterine arterial bed to maintain blood supply to the fetal-placental unit. In complicated pregnancies, inadequate remodeling of the uterine arteries, hormonal imbalances, and pre-existing conditions such as obesity, hypertension, diabetes etc. may lead to maladaptations of the uterine vasculature that includes increased vasoconstriction and endothelial dysfunction. Ultimately, uterine artery dysfunction results in increased vascular resistance impeding blood flow to the fetal-placental unit and limiting fetal growth and development. A strong association exists between poor fetal development in utero and later life health issues, which can include obesity, poor neurological development, and enhanced susceptibility to cardiovascular disease. Therefore, the detrimental outcomes of a complicated pregnancy are far-reaching and significantly impact the health of the population as a whole. Many treatment options to improve maternal uterine artery function and ameliorate the impact on the fetus are being considered. A particular difficulty in treating complicated pregnancies is the presence of not 1 but (at least) 2 patients. Novel approaches are required to successfully improve pregnancy outcomes and minimize the impact on later life health.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.035
GPT teacher head0.325
Teacher spread0.289 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2017
Admission routes1
Has abstractyes

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