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Record W2150400047 · doi:10.1080/19338240903240228

Components of Particulate Air Pollution and Emergency Department Visits in Chile

2009· article· en· W2150400047 on OpenAlexaff
Sabit Cakmak, Robert Dales, Timur Gülteki̇n, Claudia Blanco Vidal, Marcelo Farnendaz, Marı́a A. Rubio, Pedro Oyola

Bibliographic record

VenueArchives of Environmental & Occupational Health · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsStatistics CanadaUniversity of OttawaHealth Canada
Fundersnot available
KeywordsParticulatesAir pollutionEmergency departmentEnvironmental healthConfidence intervalMedicinePopulationAir pollutantsEnvironmental scienceInternal medicineChemistry

Abstract

fetched live from OpenAlex

The objective of the present study was to determine the association between several elements of particulates and Emergency Department (ED) visits in a general population sample. Daily time-series analyses tested the association between daily ED visit and air pollutants and components of particulates measured in Santiago Centro, a municipality, which includes downtown Santiago during the period from 2001 to 2006. The strongest individual effect was seen for elemental carbon. A 4.76 microg/m3 increase was associated with a relative risk (RR) of 1.12 (95% confidence interval [CI] = 1.10-1.14) increase in total ED visits, and a RR of 1.18 (95% CI = 1.16-1.21) for respiratory ED visit. Using factor analysis, the authors determined that traffic combustion-related particulates were significantly associated with ED visits. Among all the sources identified, traffic combustion-related particulates had the strongest association with ED visits. A factor indicating soil-sourced particles had a weaker but statistically significant observed morbidity effect. Of the many components of particulate air pollution, those from motor vehicle exhaust had the greatest observed effect on morbidity.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.312
Teacher spread0.287 · 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

Citations34
Published2009
Admission routes1
Has abstractyes

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