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Record W2187361134

Association between blood lead and blood pressure: Results from the Canadian Health Measures Survey (2007 to 2011).

2014· article· en· W2187361134 on OpenAlexaffabout
Tracey Bushnik, Patrick Levallois, Todd J. Anderson, Finlay A. McAlister

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsUniversity of AlbertaHealth CanadaInstitut National de Santé Publique du QuébecStatistics Canada
Fundersnot available
KeywordsBlood pressureLead (geology)MedicineEnvironmental healthAssociation (psychology)PsychologyBiologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Hypertension is the leading risk factor for cardiovascular disease, but its cause is not always known. Interest is increasing in the potential role of environmental chemicals, including lead. DATA AND METHODS: Data are from the first two cycles of the Canadian Health Measures Survey. Lead in whole blood (PbB), and systolic (SBP) and diastolic (DBP) blood pressure were measured and hypertension status was derived for 4,550 respondents aged 40 to 79. Linear regression estimated associations between PbB and SBP and DBP. Logistic regression estimated associations between PbB and hypertension. Adjusted least squares geometric means of PbB were estimated for hypertensive versus non-hypertensive individuals. RESULTS: Compared with non-hypertensive individuals, those with hypertension had higher average PbB levels, were older, more likely to be male, and more likely to have other hypertension risk factors (diabetes, family history of high blood pressure). In adjusted regression models, a modest association emerged between PbB levels and SBP among 40- to 54-year-olds, and between PbB levels and DBP for the overall population. No association emerged between PbB levels and hypertension prevalence. INTERPRETATION: A modest association was observed between blood lead levels and blood pressure, but not with hypertension, in Canadian adults aged 40 to 79.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.092
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.220
Teacher spread0.186 · 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.

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

Citations9
Published2014
Admission routes2
Has abstractyes

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