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Record W4322487814 · doi:10.1016/j.cjca.2023.02.018

Too Loud to Handle? Transportation Noise and Cardiovascular Disease

2023· review· en· W4322487814 on OpenAlexvenueno aff
Thomas Münzel, Hendrik Treede, Omar Hahad, Andreas Daiber

Bibliographic record

VenueCanadian Journal of Cardiology · 2023
Typereview
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
FundersUniversitätsmedizin der Johannes Gutenberg-Universität MainzBoehringer Ingelheim StiftungStiftung Mainzer HerzDeutsches Zentrum für Herz-Kreislaufforschung
KeywordsMedicineDiseaseEndothelial dysfunctionOxidative stressNoise (video)Stroke (engine)CardiologySleep (system call)Internal medicine

Abstract

fetched live from OpenAlex

The World Health Organization reported that more than 1.6 million healthy life-years are lost yearly from traffic-related noise in western Europe. In addition, the number of studies on health side effects in response to traffic noise is steadily growing, mainly cardiovascular disease, such as acute and chronic ischemic heart disease, heart failure, arrhythmia, and stroke. Pathophysiologically nighttime noise has been shown to cause sleep disturbances, including too short sleep periods and frequent interruption of sleep leading to an increase in the levels of circulating stress hormones and subsequently to a significant increase in the production of reactive oxygen species (oxidative stress) and inflammation in the vasculature and the brain. The consequence is arterial hypertension and vascular (endothelial) dysfunction, which might increase the risk of cardiovascular disease. With the present review, we give an overview of the "so-called" nonauditory cardiovascular health effects of noise, which have been proposed to be responsible for the future development of cardiovascular disease. We present epidemiological evidence but also evidence provided by translational human and experimental noise studies. Finally, we discuss manoeuvres to mitigate noise effectively.

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)0.002
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 score0.837
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.101
GPT teacher head0.392
Teacher spread0.291 · 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

Citations14
Published2023
Admission routes1
Has abstractyes

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