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Record W2090380590 · doi:10.1080/10641950600912984

New-Onset Hypertension in Late Pregnancy is Associated with Lower Fetal and Infant Mortality in Preterm Twins

2006· article· en· W2090380590 on OpenAlexaff
Xi-Kuan Chen, Shi Wu Wen, Graeme N. Smith, Qiuying Yang, Mark Walker

Bibliographic record

VenueHypertension in Pregnancy · 2006
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsKingston General HospitalQueen's UniversityUniversity of Ottawa
Fundersnot available
KeywordsMedicineObstetricsOdds ratioFetusPregnancyInfant mortalityConfoundingCohort studyCohortTwin PregnancyRetrospective cohort studyPediatricsPopulationInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the association between new-onset hypertension in late pregnancy (NOH) and fetal and infant mortality in early preterm, late preterm, and full-term twins. METHODS: We conducted a retrospective cohort study in 275, 316 twins in 1995-1997 based on multiple birth registration dataset of USA. Generalized estimating equations (GEEs) was used to evaluate the odds ratios (OR) of fetal and infant death (at individual level) associated with NOH, with adjustment of potential confounders at both twin set level and individual level. RESULTS: The risks for early neonatal death (OR = 0.52, 95% CI: 0.36, 0.76) and late neonatal death (OR = 0.57, 95% CI: 0.37, 0.87) were decreased in early preterm twins born to mothers with NOH compared with early preterm twins born to mothers with normal blood pressure. The decreased risks for fetal death (OR = 0.40, 95% CI: 0.30, 0.53; OR = 0.46, 95% CI: 0.53, 0.65) and infant death (OR = 0.35, 95% CI: 0.28, 0.44; OR = 0.68, 95% CI: 0.51, 0.91) were associated with NOH in both early and late preterm twins, whereas no association between NOH and fetal/infant mortality were observed in full-term twins. CONCLUSION: NOH is associated with lower risk of fetal death and infant death in preterm twins.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.249
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.

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

Citations8
Published2006
Admission routes1
Has abstractyes

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