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Record W4308625500 · doi:10.1126/sciadv.abn5164

Childhood lead exposure is associated with lower cognitive functioning at older ages

2022· article· en· W4308625500 on OpenAlexaboutno aff
Haena Lee, Mark Lee, John Robert Warren, Joseph P. Ferrie

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Aging
KeywordsLead exposureCognitionEnvironmental healthLead poisoningLead (geology)Affect (linguistics)Psychological interventionGerontologyQuarter (Canadian coin)PsychologyLife course approachCognitive declineMedicineDevelopmental psychologyGeographyPsychiatryDementiaDisease

Abstract

fetched live from OpenAlex

The Flint, Michigan water crisis renewed concern about lead toxicity in drinking water. While lead in drinking water has been shown to negatively affect cognition among children, much less is known about its long-term consequences for late-life cognition. Using a nationally representative sample of U.S. older adults linked to historical administrative data from 1940, we find that older adults who lived as children in cities with lead pipes and acidic or alkaline water-the conditions required for lead to leach into drinking water-had worse cognitive functioning but not steeper cognitive decline. About a quarter of the association between lead and late-life cognition was accounted for by educational attainment. Within the next 10 years, American children exposed to high levels of lead during the 1970s will enter older ages. Our evidence highlights the need for stronger actions to identify interventions to mitigate long-term damage among people at high risk.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.008
GPT teacher head0.231
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations54
Published2022
Admission routes1
Has abstractyes

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Same venueScience AdvancesSame topicHeavy Metal Exposure and ToxicityFrench-language works237,207