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Record W2951180814 · doi:10.1093/pch/pxz080

Lead toxicity with a new focus: Addressing low-level lead exposure in Canadian children

2019· article· en· W2951180814 on OpenAlexaffabout
Irena Buka, C Hervouet-Zeiber

Bibliographic record

VenuePaediatrics & Child Health · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsCanadian Paediatric Society
Fundersnot available
KeywordsEnvironmental healthLead exposureLead poisoningMedicineBiomonitoringLead (geology)Public healthEarly childhoodPediatricsPsychiatryPsychologyPathologyBiology

Abstract

fetched live from OpenAlex

Although acute and subacute lead toxicity requiring active treatment is rare in Canada, paediatricians need to be aware of new evidence linking lower lead levels to neurodevelopmental deficits. Health Canada has identified potential exposures occurring prenatally, in infancy and during early childhood from food and water, household dust, soil, and mouthing products that contain lead. Children with neurodevelopmental problems who live in older housing and newcomers to Canada may be at greater risk. Symptoms may be latent, subtle, and chronic. Blood assay, widely available clinically, does not confirm diagnosis unless lead exposure has been recent. Human biomonitoring data are available in the Canadian Health Measures Survey, and Health Canada has suggested that clinicians use these new reference ranges. Early detection and prevention of lead exposure are key public health objectives because effects of lead toxicity are chronic and treatment is complex.

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)0.001
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 score0.042
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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