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Association Between Preeclampsia and Congenital Heart Defects

2016· article· en· W2518165542 on OpenAlexaff
Nathalie Auger, W.D. Fraser, Jessica Healy‐Profitós, Laura Arbour

Bibliographic record

VenueObstetric Anesthesia Digest · 2016
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsMedicinePreeclampsiaPlacental growth factorEndoglinSoluble fms-like tyrosine kinase-1Vascular endothelial growth factorPlacentaBiomarkerPregnancyInternal medicineCardiologyEndothelial dysfunctionFetusEndocrinologyVEGF receptorsBiology

Abstract

fetched live from OpenAlex

( JAMA. 2015;314(15):1588–1598) Congenital heart defects are the most common anomaly in infants and are a major cause of infant morbidity and mortality. The origins of congenital heart defects, therefore, need to be better studied in order to improve prevention and earlier detection. Recent studies have tried to identify biomarkers for congenital heart defects as well as preeclampsia. Biomarker findings associated with heart defects include imbalances in proangiogenic signaling proteins, such as vascular endothelial growth factor and placental growth factor, and antiangiogenic proteins, such as soluble endoglin and fms-like tyrosine kinase 1. Imbalances in these same biomarkers have been found with preeclampsia, where there is an excess of soluble endoglin and fms-like tyrosine kinase 1 relative to placental growth factor and vascular endothelial growth factor. Angiogenic mechanisms may be part of a shared pathologic pathway for preeclampsia and congenital heart defects. Studies have shown that the pathologic changes associated with preeclampsia begin early in pregnancy, around the time that fetal heart development is occurring. This current study tried to determine the relationship between preeclampsia and the prevalence of congenital heart defects in infants whose mothers had the disorder.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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