Acetaminophen use during pregnancy and offspring attention deficit hyperactivity disorder – a longitudinal sibling control study
Bibliographic record
Abstract
Background: Maternal acetaminophen use during pregnancy is associated with increased risk of ADHD in the child. This could reflect causal influence of acetaminophen on fetal neurodevelopment or could be due to confounding factors. The aim of the current study was to examine unmeasured familial confounding factors of this association. Methods: We used data from 26,613 children from 12,902 families participating in the prospective Norwegian Mother, Father, and Child Cohort Study (MoBa). The MoBa was linked to the Norwegian Medical Birth Register and the Norwegian Patient Registry. Siblings discordant for prenatal acetaminophen exposure were compared regarding risk of having an ADHD diagnosis. Results: Children exposed to acetaminophen up to 28 days during pregnancy did not have increased risk of receiving an ADHD diagnosis compared to unexposed children. The adjusted Hazard ratio (aHR) was 0.87 (95% C.I. = 0.70-1.08) for exposure 1 to 7 days, and 1.13 (95% C.I. = 0.82-1.49) for 8-28 days. Long-term exposure (29 days or more) was associated with a two-fold increase in risk of ADHD diagnosis (aHR = 2.02, 95% C.I = 1.17-3.25). In the sibling control model, the association between long-term acetaminophen use and ADHD in the child was aHR = 2.77 (95% C.I. = 1.48-5.05) at the between-family level, and aHR = 1.06 (95% C.I. = 0.51-2.05) at the within-family level. Conclusions: Both the exposed and the unexposed children of mothers with long-term use of acetaminophen in one of the pregnancies had increased risk of receiving an ADHD diagnosis. This indicates that the observed association between long-term acetaminophen use during pregnancy and ADHD in the child may at least partly be confounded by unobserved family factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".