Maternal and offspring outcomes associated with prescribed ADHD medication in pregnancy: a systematic review
Bibliographic record
Abstract
PURPOSE: During pregnancy, it is unclear whether women with attention deficit hyperactivity disorder (ADHD) should stop prescribed medication - risking relapse - or continue - risking harm to themselves and their baby. We aimed to conduct a systematic review to examine whether ADHD medications should be continued during pregnancy. METHODS: We searched MEDLINE, Embase, PsycINFO, PubMed, CINAHL, AMED, CENTRAL, Cochrane Library, NHS Knowledge and Library Hub from 1st July 2019 to 1st July 2024, without any restrictions on language, setting, or study type. We supplemented this with relevant studies identified from the references of retrieved studies. Two authors used the Newcastle-Ottawa Scale (NOS) to independently rate the quality of included studies. RESULTS: Twelve cohort studies were included in the qualitative review. All were deemed high quality (NOS ≥ 7). Seven studies found ADHD medication use during pregnancy had no significant negative effect on maternal or offspring outcomes. One study found continuing ADHD medication reduced the risk of various negative outcomes, and another found stopping ADHD medication may increase the risk of threatened abortion. Three studies concluded that ADHD medication use was associated with negative outcomes: pre-eclampsia, gastroschisis, omphalocele, and transverse limb deficiency. Modafinil was identified as significantly increasing the risk of congenital malformations. CONCLUSION: Women taking modafinil should consider stopping it prior to pregnancy. Clinicians should discuss the risks, benefits, and uncertainties of other ADHD medications with women who are pregnant, or considering pregnancy, keeping in mind that the benefits of continuing ADHD medications- where it is effective for an individual- are likely to outweigh the risks.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".