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Record W2899004655 · doi:10.1055/s-0038-1675374

Effects of Preeclampsia on Maternal and Pediatric Health at 11 Years Postpartum

2018· article· en· W2899004655 on OpenAlexaff
Stephanie E. Chan, Jessica Pudwell, Graeme N. Smith

Bibliographic record

VenueAmerican Journal of Perinatology · 2018
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineInterquartile rangePreeclampsiaPregnancyProspective cohort studyPediatricsCohortObstetricsBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

Objective To determine the association of preeclampsia (PE) with the presence of cardiovascular risk (CVR) factors at approximately 11 years postpartum and to assess the longer term effect of PE on childhood development. Study Design A mail-out survey was sent to all women who were previously recruited into the Kingston arm of the Pre-Eclampsia New Emerging Team's prospective cohort (n = 112 PE and n = 150 control). Physical and biochemical CVR markers were compared between the two groups. Physical, social, and cognitive development of the children involved in the pregnancies was evaluated using the Vineland-3 Domain-Level Parent/Caregiver Form. Results Thirteen PE women and 28 control women returned the study questionnaire. Based on the 2017 American Heart Association High Blood Pressure Clinical Practice Guidelines, 9/13 (69.2%) of the PE women, compared with 6/28 (21.4%) of the control women, have hypertension (p < 0.01). The median percentile rank for overall adaptive functioning was 58 (interquartile range [IQR: 21–73]) in the PE children and 81 (IQR: 61–94) in the control children (p < 0.05). Conclusion The development of PE leads to longer term changes in CVR markers and in childhood development at approximately 11 years postpartum. Pregnancy and the postpartum provide an early window of opportunity for early maternal and child screening and intervention for health preservation and disease prevention.

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 score0.072
Threshold uncertainty score0.378

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 studies0.0000.001
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 head0.007
GPT teacher head0.274
Teacher spread0.267 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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