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Enregistrement W2783468463 · doi:10.1111/1471-0528.15118

Antidepressant use during pregnancy and <scp>ADHD</scp> risk in children: current knowledge

2018· letter· en· W2783468463 sur OpenAlexaff
Takoua Boukhris

Notice bibliographique

RevueBJOG An International Journal of Obstetrics & Gynaecology · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueMaternal Mental Health During Pregnancy and Postpartum
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésPregnancyConfoundingAttention deficit hyperactivity disorderImpulsivityAntidepressantMedicinePsychiatryPsychologyAnxietyInternal medicineBiology

Résumé

récupéré en direct d'OpenAlex

Antidepressants (ADs) are among the most frequently used medications in pregnancy. ADs can cross the placental barrier (Rampono et al. Pharmacopsychiatry 2009;42:95–100; Loughhead et al. Biol Psychiatry 2006;59:287–90), resulting in a dysfunction of the serotonergic and norepinephrine systems, which could cause inattention or hyperactivity-impulsivity behaviours, as exhibited in attention deficit disorder with/without hyperactivity (ADHD). Several epidemiological studies have investigated the potential link between AD exposure during pregnancy and the risk of ADHD in children; however, the findings have been conflicting. In the current study (Jiang et al.), the authors have conducted a systematic review and meta-analysis of six cohort studies to evaluate the association between AD use in utero and the risk of ADHD in children. By performing a meta-analysis, which is a useful method to quantify an overall association by pooling all of the results from the literature, authors have shown that overall AD exposure, specifically to selective serotonin reuptake inhibitors (SSRIs), was associated with an increased risk of ADHD in children. Authors performed several subanalyses in an attempt to reduce within-study heterogeneity. Furthermore, to address potential confounding by genetic profiles and socio-economic factors, they pooled two studies that had performed sibling-matched analyses. Although this may not provide us with sufficient evidence on the matter, it still gives added value to the paper. Jiang et al. further attempted to take into consideration the impact of confounding by indication by analysing different comparison groups (AD exposure during pregnancy versus pre-pregnancy exposure; maternal psychiatric disorder without exposure versus no exposure; AD use during pregnancy versus maternal psychiatric disorder without drug use); however, the authors have suggested that the significant association they observed between AD exposure during pregnancy and ADHD can be explained partially by confounding by indication. The results remain inconsistent given the low number of studies included and the authors acknowledged that we need further investigation before interpreting these findings. Nevertheless, we cannot exclude completely confounding by severity of depression. Although all included studies were of high quality, based on the methodological quality assessment scores as recommended by the Cochrane Collaboration, the results should be interpreted with caution given the inherent limitations of observational studies. Future studies will need to address the limitations of small sample sizes for subgroup analyses and controlling adequately for confounding by indication and severity. Nevertheless, this meta-analysis provides relevant information useful in the evaluation of the risk of ADHD associated with AD use during pregnancy. Indeed, doctors need to consider the impact of the use of such drugs during pregnancy on ADHD in children and balance this against the risk of exposing the fetus to psychiatric maternal illness. Detailed knowledge of AD use in pregnancy, specifically according to drug types and dosage, and the risk of ADHD are pivotal to implementing clinical strategies in order to address whether a mother should be treated for her psychiatric illness during pregnancy in terms of the impact of this treatment on childhood development, including ADHD. None declared. Completed disclosure of interests form available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,007
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0020,003
Bibliométrie0,0020,003
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,031
Tête enseignante GPT0,322
Écart entre enseignants0,291 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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