Antidepressant use during pregnancy and <scp>ADHD</scp> risk in children: current knowledge
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".