Prevalence of the Prescribing of Attention-Deficit Hyperactivity Disorder Medication During Pregnancy: A Systematic Review
Bibliographic record
Abstract
Attention deficit hyperactivity disorder (ADHD) diagnosis in women has markedly increased in recent years, resulting in an increase of ADHD medication exposure during pregnancy, which has uncertain health risks. It is therefore critical to understand the extent of the issue. The objective of this novel systematic review was to investigate the prevalence of the prescribing of ADHD medication during pregnancy. A systematic review was conducted following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The last search was October 13, 2023, for articles examining the prescribing of ADHD medication in pregnancy. Fifteen studies were included. Participants from the United States (n = 4), the Nordic countries (n = 10), Canada (n = 3), and Australia (n = 1) were included. The prevalence of ADHD-medication dispensing during pregnancy ranged from 0.07 to 6.01 per 1,000 pregnancies. Typically, stimulant medications were more prevalent than nonstimulants. The most prevalent ADHD medication dispensed during pregnancy was methylphenidate followed by amphetamine-type medications. There was high heterogeneity (I2 = 99.95%) between the studies, making the study data not appropriate for meta-analysis. The prevalence of the prescribing of ADHD medications in pregnancy ranged substantially, with psychostimulant medications most prescribed. Apart from in the Nordic countries, estimates of prevalence were limited.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| 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".