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Record W2989966774 · doi:10.1289/isee.2013.p-3-07-16

Traffic related air pollution and noise on cardiovascular outcomes: a systematic review

2013· review· en· W2989966774 on OpenAlexaff
Louis-François Tétreault, Stéphane Perron, Audrey Smargiassi

Bibliographic record

VenueISEE Conference Abstracts · 2013
Typereview
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsConfoundingEnvironmental healthMedicineAir pollutantsPollutantAir pollutionTraffic noiseCardiovascular healthEnvironmental scienceBiologyInternal medicineComputer scienceEcology

Abstract

fetched live from OpenAlex

Objective: This study financed by the National Collaborating Centre for Environmental Health aim of this review was to assess the confounding effect of one traffic-related exposure (noise or air pollutants) on the association between the other exposure and cardiovascular outcomes. Methods: A systematic review of English and French literature was conducted with the bibliographic databases Medlines and Embase. The confounding effects in studies were assessed by using the change in the point estimate with a cut-off point 10%. Results: The literature search yield 180 articles; 9 articles met our selection criteria. For most studies, the modification of the association between traffic-related noise or air pollutants vis-à-vis cardiovascular outcomes produced changes in point estimates lower than 10%. However, the results were inconsistent when assessing the effects on blood pressure, which may underlie the presence of a small confounding effect. Conclusion: The results from this review suggest that important confounding of cardiovascular effects by traffic-related noise or reported air pollutants is unlikely. Studies with a standardized methodology and assessing the effects of specific traffic-related pollutants are needed to properly assess confounding effects.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.346
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.005

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 head0.097
GPT teacher head0.401
Teacher spread0.304 · 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.

Study designSystematic review
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

Citations0
Published2013
Admission routes1
Has abstractyes

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