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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 teacher head, not a consensus.

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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