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Record W4400912620 · doi:10.1038/s41372-024-02058-3

Adverse fetal/neonatal and obstetric outcomes in pregnancies with both maternal and fetal heart disease

2024· article· en· W4400912620 on OpenAlexaff
Beatriz A. Fernández-Campos, Jasmine Grewal, Marla Kiess, Samuel C. Siu, Birgit Pfaller, Mathew Sermer, Jennifer Mason, Candice Silversides, Kim Haberer

Bibliographic record

VenueJournal of Perinatology · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsMount Sinai HospitalWestern UniversitySt. Paul's HospitalUniversity of British ColumbiaToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineFetusObstetricsPreeclampsiaGestational agePregnancyHeart diseaseSmall for gestational ageGestationInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate fetal/neonatal and obstetric events in pregnancies with both maternal and fetal heart disease. STUDY DESIGN: From the CARPREG database, singleton pregnancies (>24 weeks) in patients with structural heart disease that underwent fetal/neonatal echocardiograms were selected and separated in two groups: maternal heart disease only (M-HD) and maternal and fetal heart disease (MF-HD). Differences in adverse fetal/neonatal (death, preterm birth, and small for gestational age) and obstetric (preeclampsia/eclampsia) outcomes between groups were analyzed. RESULTS: From 1011 pregnancies, 93 had MF-HD. Fetal/neonatal events (38.7% vs 25.3%, p = 0.006) and spontaneous preterm birth (10.8% vs 4.9%, p = 0.021) were more frequent in MF-HD compared to M-HD, with no difference in obstetric events. MF-HD remained as a significant predictor of fetal/neonatal events after adjustment (OR:1.883; 95% CI:1.182-3.000; p = 0.008). CONCLUSIONS: Pregnancies with MF-HD are at risk of adverse fetal/neonatal events and spontaneous preterm birth. Larger studies are needed to determine their association with preeclampsia.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.262
Teacher spread0.254 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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