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Record W2102103753 · doi:10.1080/08958370600904538

Association between Air Pollution and Multiple Respiratory Hospitalizations among the Elderly in Vancouver, Canada

2006· article· en· W2102103753 on OpenAlexaffabout
Karen Y. Fung, Shahedul A. Khan, Daniel Krewski, Yue Chen

Bibliographic record

VenueInhalation Toxicology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsInstitute of Population and Public HealthUniversity of OttawaUniversity of Windsor
Fundersnot available
KeywordsAir pollutionEnvironmental healthMedicineAssociation (psychology)Air pollutantsGeographyDemographyPsychologyBiology

Abstract

fetched live from OpenAlex

Recurrent events, such as repeated hospital admissions for the same health outcome, occur frequently in environmental health studies. In this study, we conducted an analysis of data on repeated respiratory hospitalizations among the elderly in Vancouver, Canada, for the period of June 1, 1995, to March 31, 1999, using a new method proposed by (Dewanji and Moolgavkar 2000, 2002) for recurrent events, and compared it with some traditional methods. In particular, we assessed the impact of ambient gaseous (SO2, NO2, CO, and O3) and particulate pollutants (PM10, PM2.5, and PM10-2.5) as well as the coefficient of haze (CoH) on recurrent respiratory hospital admissions. Using the new procedure, significant associations were found between admissions and 3-day, 5-day, and 7-day moving averages of the ambient SO2 concentrations, with the strongest association observed at the 7-day lag (RR = 1.044, 95% CI: 1.018-1.070). We also found PM10-2.5 for 3-day and 5-day lag to be significant, with the strongest association at 5-day lag (RR = 1.020, 95% CI: 1.001-1.039). No significant associations with admission were found with current day exposure.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.026
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.250
Teacher spread0.236 · 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

Citations31
Published2006
Admission routes2
Has abstractyes

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