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Record W1518380443 · doi:10.1002/oby.20748

Obesity and the cardiovascular health effects of fine particulate air pollution

2014· review· en· W1518380443 on OpenAlexaff
Scott Weichenthal, Jane A. Hoppin, François Reeves

Bibliographic record

VenueObesity · 2014
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversité de MontréalHealth Canada
Fundersnot available
KeywordsMedicineObesityBody mass indexCardiovascular healthEpidemiologyCohort studyEnvironmental healthCohortProspective cohort studyBlood pressurePublic healthGerontologyInternal medicineDiseasePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: This review examines evidence related to the potential impact of obesity on the cardiovascular health effects of fine particulate air pollution (PM₂.₅). METHODS: A PubMed search was conducted in December, 2013 and studies were included if they examined the relationship between PM₂.₅ and cardiovascular health as well as effect modification by obesity. RESULTS: One hundred twenty-one citations were reviewed; three large prospective cohort studies and 14 panel studies with short-term follow-up met the above criteria. All three cohort studies reported stronger associations between PM₂.₅ and cardiovascular mortality among obese subjects and one reported a significant trend of increased risk with increased body mass index. Similarly, 11 of 14 panel studies reported stronger associations between PM₂.₅ and acute changes in physiological measures of cardiovascular health among obese subjects including outcomes such as blood pressure and arrhythmia. Although interactions were not always statistically significant, the consistent pattern of stronger associations among obese subjects suggests that obesity may modify the impact of PM2.5 on cardiovascular health. CONCLUSIONS: Epidemiological evidence suggests that obesity may increase susceptibility to the cardiovascular health effects of PM₂.₅. This an important area of research as the public health impacts of air pollution could increase with increasing prevalence of obesity.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.026
GPT teacher head0.302
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations107
Published2014
Admission routes1
Has abstractyes

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