Acetaminophen use during pregnancy and the risk of attention deficit hyperactivity disorder: A causal association or bias?
Bibliographic record
Abstract
BACKGROUND: The association between acetaminophen use during pregnancy and the development of attention deficit hyperactivity disorder (ADHD) in the offspring may be due to bias. OBJECTIVES: The primary objective was to assess the role of potential unmeasured confounding in the estimation of the association between acetaminophen use during pregnancy and the risk of ADHD, through bias analysis. The secondary objective was to assess the roles of selection bias and exposure misclassification. DATA SOURCES: We searched MEDLINE, Embase, Scopus, and the Cochrane Library up to December 2018. STUDY SELECTION AND DATA EXTRACTION: We included observational studies examining the association between acetaminophen use during pregnancy and the risk of ADHD. SYNTHESIS: We meta-analysed data across studies, using random-effects model. We conducted a bias analysis to studies that did not adjust for important confounders, to explore systematic errors related to unmeasured confounding, selection bias, and exposure misclassification. RESULTS: = 48%). Sensitivity analysis for unmeasured confounding in this meta-analysis showed that a confounder of 1.69 on the RR scale would reduce to 10% the proportion of studies with a true effect size of RR >1.10. Unmeasured confounding bias analysis decreased the point estimate in five of the seven studies and increased in two studies, suggesting that the observed association could be confounded by parental ADHD. Unadjusted and bias-corrected risk ratios (bcRRs) were: RR = 1.34, bcRR = 1.13; RR = 1.51, bcRR = 1.17; RR = 1.63, bcRR = 1.38; RR = 1.44, bcRR = 1.17; RR = 1.16, bcRR = 1.18; RR = 1.25, bcRR = 1.05; and RR = 0.99, bcRR = 1.18. CONCLUSIONS: Bias analysis suggests that the previously reported association between acetaminophen use during pregnancy and an increased risk of ADHD in the offspring may be due to unmeasured confounding. Our ability to conclude a causal association is 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 | 0.146 | 0.368 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".