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Record W2015541979 · doi:10.1016/j.envpol.2014.03.004

Metal composition of fine particulate air pollution and acute changes in cardiorespiratory physiology

2014· article· en· W2015541979 on OpenAlexafffund
Sabit Cakmak, Robert Dales, Lisa Marie Kauri, Mamun Mahmud, Keith Van Ryswyk, Jennifer Vanos, Ling Liu, Premkumari Kumarathasan, Errol M. Thomson, Renaud Vincent, Scott Weichenthal

Bibliographic record

VenueEnvironmental Pollution · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsHealth Canada
FundersHealth CanadaAlgoma University
KeywordsCardiorespiratory fitnessCadmiumInterquartile rangeParticulatesRespiratory systemPhysiologyHeart rateEnvironmental chemistryMedicineChemistryBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Studying the physiologic effects of components of fine particulate mass (PM2.5) could contribute to a better understanding of the nature of toxicity of air pollution. OBJECTIVES: We examined the relation between acute changes in cardiovascular and respiratory function, and PM2.5-associated-metals. METHODS: Using generalized linear mixed models, daily changes in ambient PM2.5-associated metals were compared to daily changes in physiologic measures in 59 healthy subjects who spent 5-days near a steel plant and 5-days on a college campus. RESULTS: Interquartile increases in calcium, cadmium, lead, strontium, tin, vanadium and zinc were associated with statistically significant increases in heart rate of 1-3 beats per minute, increases of 1-3 mmHg in blood pressure and/or lung function decreases of up to 4% for total lung capacity. CONCLUSION: Metals contained in PM2.5 were found to be associated with acute changes in cardiovascular and respiratory physiology.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.249
Teacher spread0.234 · 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

Citations190
Published2014
Admission routes2
Has abstractyes

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