The Debate on Acetaminophen Use in Pregnancy and Neurodevelopmental Disorders: Facts or Fiction?
Bibliographic record
Abstract
OBJECTIVES: This study aimed to determine if the use of acetaminophen alone and in combination during the second and third trimesters of pregnancy is associated with the risk of attention-deficit/hyperactivity disorder (ADHD) in children, and to evaluate uncertainty from exposure and outcome misclassification. METHODS: We included all singleton live births from the Quebec Pregnancy Cohort between January 1, 1998, and December 31, 2013. Maternal acetaminophen use was identified through filled prescription data, and children were classified into 3 exposure groups: (1) unexposed, (2) exposed to acetaminophen alone, and (3) exposed to acetaminophen in combination with other medications during the second or third trimester of pregnancy. ADHD was assessed in children aged ≥2 years using a validated algorithm: 2 diagnostic codes, 2 filled prescriptions for ADHD medication, or 1 diagnostic code plus 1 filled prescription. To address potential non-differential exposure and outcome misclassification, we conducted a probabilistic bias analysis using individual-level data, which represents the central contribution of this study. RESULTS: Among the 182 775 children included, 1.0% were exposed to acetaminophen alone and 2.2% to acetaminophen in combination with other medications. In Cox proportional hazard models, acetaminophen use in combination was associated with increased risk of ADHD (adjusted hazard ratio 1.17; 95% CI 1.06-1.29), while acetaminophen alone showed a weaker association (adjusted hazard ratio 1.09; 95% CI 0.94-1.27). Probabilistic bias analysis demonstrated that these estimates might be biased away from the null. CONCLUSIONS: Our findings suggest that the observed relationship might be partly explained by exposure and outcome misclassification.
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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 | 0.025 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.031 | 0.043 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".