Lead exposure and antisocial behavior: A systematic review of human and animal evidence
Bibliographic record
Abstract
BACKGROUND: Despite decades of research and interventions, lead (Pb) exposure remains a global public health concern. In addition to well-documented impacts on cognition, there is growing evidence of Pb's impacts on antisocial behaviors, including aggression, conduct or antisocial disorders, and violation of social norms. We conduct a systematic review on the association between Pb and antisocial behavior from human and animal data. METHODS: We followed our protocol with selected modifications for practicality. Peer-reviewed epidemiology and toxicology literature from PubMed, BIOSIS, and Web of Science were searched through June 2024 and screened for relevance, leveraging machine-learning. Details for each Population, Exposure, Comparator, Outcome (PECO)-relevant study were summarized. Studies were evaluated for potential bias and sensitivity according to predefined metrics through the Health Assessment Workspace Collaborative (HAWC) system. Evidence was synthesized by sub-outcome (human: aggression; antisocial diagnoses or domains; violation of social norms; animal: aggression; social behavior) and then integrated across evidence streams, based on approaches adapted from the U.S. EPA. RESULTS: More than 15,000 studies were identified. After screening and scoping refinements, 43 epidemiological and 37 animal studies were included for narrative review. In the epidemiological database, there was lack of comparability in outcome assessment methods, precluding quantitative meta-analysis. Human and animal evidence for impacts on aggression was slight. Human and animal evidence for impacts on antisocial-related disorders or domains and social behavior, respectively, was moderate. Human evidence for impacts on violation of social norms was moderate. CONCLUSIONS: From our updated review of epidemiological and toxicological data, we find that evidence indicates a likely causal association between Pb and antisocial behavior.
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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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".