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Record W4416867655 · doi:10.1016/j.jogc.2025.103192

Acetaminophen Use During Pregnancy and Neurodevelopmental Risk: Biological Plausibility

2025· article· en· W4416867655 on OpenAlexaffvenue
Delaine Pereira, Anick Bérard, Justine Pleau, Lisiane Freitas Leal, Louise M. Winn

Bibliographic record

VenueJournal of Obstetrics and Gynaecology Canada · 2025
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineQueen's University
FundersCenter for Truth in Science
KeywordsPregnancyAcetaminophenAnimal studiesHuman studiesMEDLINEPresentation (obstetrics)DrugEpigenetics

Abstract

fetched live from OpenAlex

In May of 2025, a Canadian Mother-Child Initiative on Drug Safety in Pregnancy symposium was held during the Canadian National Perinatal Research Meeting in Montréal, Québec. This symposium included a presentation covering the mechanistic evidence on the biological plausibility of acetaminophen use during pregnancy affecting neurodevelopment. Three mechanistic pathways including oxidative stress, changes in placental and brain transporter expression, and alterations in epigenetic regulation were presented. Given that much of the mechanistic evidence comes from animal and in vitro studies, limitations with these models and their relevance to human pregnancy were also considered. Current evidence for both human and mechanistic studies remain too limited and inconsistent to infer a causal effect at typical clinical exposures. Therefore, the Society of Obstetricians and Gynaecologists of Canada's recommendation that "the use of acetaminophen as a first-line therapeutic option for fever and pain in pregnancy when medically indicated at recommended doses for the shortest duration required" is supported.

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)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.554

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.250
Teacher spread0.230 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes2
Has abstractno

Explore more

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