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Record W4392414766 · doi:10.2196/47396

Preeclampsia Onset, Days to Delivery, and Autism Spectrum Disorders in Offspring: Clinical Birth Cohort Study

2024· article· en· W4392414766 on OpenAlexvenueno aff
Sarah Carter, Jane C. Lin, Ting Chow, Mayra P. Martinez, Chunyuan Qiu, R. Klara Feldman, Rob McConnell, Anny H. Xiang

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsnot available
FundersNational Institute of Environmental Health Sciences
KeywordsOffspringAutismCohortMedicineCohort studyPreeclampsiaAutism spectrum disorderPregnancyObstetricsPediatricsPsychiatryBiologyGeneticsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Maternal preeclampsia is associated with a risk of autism spectrum disorders (ASD) in offspring. However, it is unknown whether the increased ASD risk associated with preeclampsia is due to preeclampsia onset or clinical management of preeclampsia after onset, as clinical expectant management of preeclampsia allows pregnant women with this complication to remain pregnant for potentially weeks depending on the onset and severity. Identifying the risk associated with preeclampsia onset and exposure provides evidence to support the care of high-risk pregnancies and reduce adverse effects on offspring. OBJECTIVE: This study aimed to fill the knowledge gap by assessing the ASD risk in children associated with the gestational age of preeclampsia onset and the number of days from preeclampsia onset to delivery. METHODS: This retrospective population-based clinical cohort study included 364,588 mother-child pairs of singleton births between 2001 and 2014 in a large integrated health care system in Southern California. Maternal social demographic and pregnancy health data, as well as ASD diagnosis in children by the age of 5 years, were extracted from electronic medical records. Cox regression models were used to assess hazard ratios (HRs) of ASD risk in children associated with gestational age of the first occurrence of preeclampsia and the number of days from first occurrence to delivery. RESULTS: Preeclampsia occurred in 16,205 (4.4%) out of 364,588 pregnancies; among the 16,205 pregnancies, 2727 (16.8%) first occurred at <34 weeks gestation, 4466 (27.6%) first occurred between 34 and 37 weeks, and 9012 (55.6%) first occurred at ≥37 weeks. Median days from preeclampsia onset to delivery were 4 (IQR 2,16) days, 1 (IQR 1,3) day, and 1 (IQR 0,1) day for those first occurring at <34, 34-37, and ≥37 weeks, respectively. Early preeclampsia onset was associated with greater ASD risk (P=.003); HRs were 1.62 (95% CI 1.33-1.98), 1.43 (95% CI 1.20-1.69), and 1.23 (95% CI 1.08-1.41), respectively, for onset at <34, 34-37, and ≥37 weeks, relative to the unexposed group. Within the preeclampsia group, the number of days from preeclampsia onset to delivery was not associated with ASD risk in children; the HR was 0.995 (95% CI 0.986-1.004) after adjusting for gestational age of preeclampsia onset. CONCLUSIONS: Preeclampsia during pregnancy was associated with ASD risk in children, and the risk was greater with earlier onset. However, the number of days from first preeclampsia onset to delivery was not associated with ASD risk in children. Our study suggests that ASD risk in children associated with preeclampsia is not increased by expectant management of preeclampsia in standard clinical practice. Our results emphasize the need to identify effective approaches to preventing the onset of preeclampsia, especially during early pregnancy. Further research is needed to confirm if this finding applies across different populations and clinical settings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.344
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2024
Admission routes1
Has abstractyes

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