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Record W2138251507 · doi:10.1080/10641950600912992

Maternal Age, Paternal Age and New-Onset Hypertension in Late Pregnancy

2006· article· en· W2138251507 on OpenAlexaff
Xi-Kuan Chen, Shi Wu Wen, Graeme N. Smith, Art Leader, Marilyn Sutandar, Qiuying Yang, Mark Walker

Bibliographic record

VenueHypertension in Pregnancy · 2006
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsQueen's UniversityKingston General HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineAdvanced maternal agePaternal agePregnancyLogistic regressionYoung adultCohortAge of onsetRetrospective cohort studyObstetricsCohort studyPediatricsDemographyOffspringInternal medicineDiseaseFetus

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the association between maternal age, paternal age, and new-onset hypertension in late pregnancy. METHODS: We carried out a retrospective cohort study of 9,302,675 pregnant women with live births in the United States between 1995 and 1998. Maternal and paternal ages were analyzed together using "couple age" in multivariate logistic regression models to reduce colinearity between maternal age and paternal age. The effect of paternal age was also analyzed with stratification of maternal age. RESULTS: Compared with couples with both a maternal and paternal age of 20 to 34 years, an older maternal age (above 35 years) was associated with an increased risk for new-onset hypertension, except for couples with a very young father (below 20 years). Younger maternal age (below 20 years) was associated with a decreased risk for new-onset hypertension, except for couples with a very old father (above 45 years). There was no significant association between paternal age and new-onset hypertension with stratification of maternal age. CONCLUSION: Increased risk for new-onset hypertension in late pregnancy is significantly associated with advancing maternal age, whereas there is no association between paternal age and new-onset hypertension in late pregnancy.

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 score0.031
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.039
GPT teacher head0.253
Teacher spread0.214 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

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