Prenatal Acetaminophen, Adverse Birth Outcomes, and ADHD: Mediation Analysis in a Prospective Cohort
Bibliographic record
Abstract
Structured Abstract Background Although we previously reported an association of prenatal acetaminophen exposure with more than two-fold increased odds of child ADHD, it is unknown whether prenatal acetaminophen is associated with adverse birth outcomes, and if birth outcomes may mediate the association of prenatal acetaminophen with ADHD. Methods This birth cohort from Sherbrooke, Québec, Canada, included 773 live births. Mothers with no thyroid disease enrolled at their first prenatal care visit or delivery. Acetaminophen was measured in meconium for 393 children at delivery. Physician diagnosis of ADHD was determined when children were 6-7 years old. We first tested associations of prenatal acetaminophen with birthweight, preterm birth, gestational age, and small and large for gestational age. Then we assessed whether these birth outcomes mediate the association of prenatal acetaminophen with ADHD. We imputed missing data via multiple imputation and used inverse probability weighting to account for confounding and selection bias. Results Acetaminophen was detected in 222 meconium samples (56.5%). Prenatal acetaminophen exposure was associated with decreased birthweight by 136 grams (β = -136; 95%CI [-229, - 43]), 20% increased weekly hazard of delivery (hazard ratio = 1.20; 95%CI [1.00, 1.43]), and over 60% decreased odds of being born large for gestational age (odds ratio = 0.38; 95%CI [0.20, 0.75]). There was no evidence for adverse birth outcomes mediating the association of prenatal acetaminophen with child ADHD. Conclusions Although unobserved confounding and confounding by indication are possible, these results warrant further investigation into adverse perinatal effects of prenatal acetaminophen exposure.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".