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Record W2940866216 · doi:10.1111/bcp.13931

Spotlight Commentary: Medicines use during pregnancy and harmful effects on offspring

2019· article· en· W2940866216 on OpenAlexaboutno aff
Li Wei, Adam F. Cohen

Bibliographic record

VenueBritish Journal of Clinical Pharmacology · 2019
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
FundersRosetrees Trust
KeywordsPregnancyMedicineOffspringOdds ratioFluoxetineObstetricsRelative riskCohort studyConfidence intervalCohortPediatricsInternal medicine

Abstract

fetched live from OpenAlex

Medicines use during the pregnancy can have a potentially negative impact on offspring because most medicines taken during pregnancy cross the placenta and reach the foetus. Safety concerns about the common medicines such as antibiotics, antidepressants, and paracetamol have been raised in postmarketing studies. A nested case-control study with 77 429 pregnancies which included 7039 spontaneous abortions and 70 390 controls in the Quebec Pregnancy Cohort was published recently in the British Journal of Clinical Pharmacology.1 They reported that trimethoprim-sulfamethoxazole (TMP-SMX) exposure during pregnancy was associated with an increased risk of spontaneous abortion (adjusted odds ratio 2.94, 95% confidence interval (CI) 1.89–4.57, 25 exposed cases). This strong association was unlikely to be biased by unmeasured confounding factors. A study conducted in France found that about a quarter of pregnant women took antidepressants2 and this highlights the potential safety concern about the offspring of these pregnant women. However, decisions regarding the use of antidepressants during pregnancy are complex and requires balancing the risks and benefits of the medications on both mother and child. The association between antidepressants in pregnancy and risk of attention-deficit/hyperactivity disorder in children has been reported in the literature.3 Zhao and colleagues conducted a systematic review and meta-analysis of cohort studies on the association between maternal fluoxetine use during the first trimester of pregnancy and congenital malformations in infants.4 Fluoxetine use was associated with increased risks of major malformations (relative risk (RR) 1.18, 95% CI, 1.08–1.29), cardiovascular malformations (1.36, 95% CI, 1.17–1.59), septal defects (1.38, 95% CI, 1.19–1.61), and non-septal defects (1.39, 95% CI, 1.12–1.73) with low heterogeneity in infants. No significant observations of other system-specific malformations were found from this review articles. Paracetamol is a commonly used medicine to treat pain and reduce a high temperature. A 25 case series review article5 reported that a causal relationship between maternal paracetamol intake and fetal ductus arteriosus constriction or closure is likely according to the World Health Organization Uppsala Monitoring Centre (WHO-UPC) causality tool (one case was classified as unlikely, nine as possible, 11 as probable and four as certain). The authors concluded that the findings suggest that pharmacovigilance studies on paracetamol safety during pregnancy are warranted to quantify the risk of such event and put the current findings into clinical perspective. The public should recognize the potential risk to offspring when pregnant women take any medicines should be highlighted to the general population. Due to the nature of observational studies, the results should be interpreted cautiously, especially any weak associations. More well designed studies are required to investigate the medicine safety issues in pregnant women. There are no competing interests to declare.

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)0.009
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.049
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.073
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0040.005
Open science0.0100.002
Research integrity0.0490.027
Insufficient payload (model declined to judge)0.0390.015

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 head0.385
Teacher spread0.352 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2019
Admission routes1
Has abstractyes

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