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Record W2044602432 · doi:10.2495/air110411

Analysis of lung cancer incidence relating to air pollution levels adjusting for cigarette smoking: a case-control study

2011· article· en· W2044602432 on OpenAlexafffundabout
Pierre R. Band, Hong Jiang, Jan M. Zielinski

Bibliographic record

VenueWIT transactions on ecology and the environment · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsHealth Canada
FundersHealth Canada
KeywordsWindsorLung cancerResidenceMedicineIncidence (geometry)Cancer registryEnvironmental healthDemographyCancerStatisticsEnvironmental scienceOncologyPopulationInternal medicineMathematics

Abstract

fetched live from OpenAlex

A case-control study (lung cancer: 2711; age and sex matched non-lung cancer controls: 2711) encompassing the years 1986-2004 was undertaken further to the results of increased mortality and cancer incidence in Windsor, Canada. The objective was to investigate associations between lung cancer incidence and exposure to air pollutants controlling for cigarette smoking and duration of residence. A nominal file of cases and controls ascertained by the Ontario Cancer Registry was obtained from Cancer Care Ontario; smoking information and addresses of residence within Windsor were obtained respectively from medical charts at the Windsor Regional Cancer Centre and from the Windsor City Directories. For each case and control and for each air pollutant, a cumulative exposure was calculated based on duration of residence at each address location; the centroid of postal codes of addresses was used as proxy for residential location. For each location, annual NO 2 and SO 2 levels were estimated, using Land Use Regression based on results from 54 monitors within Windsor. Results obtained from conditional likelihood regression for matched or stratified data, (including sex, age at diagnosis, numbers of cigarette smoked per day and duration of smoking as covariates) are presented.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.293
Teacher spread0.252 · 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 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

Citations2
Published2011
Admission routes3
Has abstractyes

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