MétaCan
Menu
Back to cohort
Record W2021468040 · doi:10.3200/aeoh.59.1.14-21

Association Between Particulate Air Pollution and First Hospital Admission for Childhood Respiratory Illness in Vancouver, Canada

2004· article· en· W2021468040 on OpenAlexaffabout
Qiuying Yang, Yue Chen, Daniel Krewski, Yuanli Shi, Richard T. Burnett, Kimberlyn McGrail

Bibliographic record

VenueArchives of Environmental Health An International Journal · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British ColumbiaInstitute of Population and Public HealthUniversity of Ottawa
Fundersnot available
KeywordsOdds ratioMedicineConfidence intervalParticulatesLogistic regressionAir pollutionRespiratory systemEnvironmental healthAir pollutantsParticulate pollutionRespiratory diseasePediatricsInternal medicine

Abstract

fetched live from OpenAlex

In this study, the authors assessed the impact of particulate air pollution on first respiratory hospitalization. Study subjects were children less than 3 years of age living in Vancouver, British Columbia, who had their first hospitalization as a result of any respiratory disease (ICD-9 codes 460-519) during the period from June 1, 1995, to March 31, 1999. The authors used logistic regression to estimate the associations between ambient concentrations of particulate matter (PM) and first hospitalization. The adjusted odds ratios for first respiratory hospitalization associated with mean and maximal PM10-2.5 with a lag of 3 days were 1.12 (95% confidence interval: 0.98, 1.28) and 1.13 (1.00, 1.27). After adjustment for gaseous pollutants, the corresponding odds ratios were 1.22 (1.02, 1.48) and 1.14 (0.99, 1.32). The data indicated the possibility of harmful effects from coarse PM on first hospitalization for respiratory disease in early childhood.

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)0.000
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 score0.030
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.273
Teacher spread0.262 · 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

Citations15
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueArchives of Environmental Health An International JournalSame topicAir Quality and Health ImpactsFrench-language works237,207