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Record W4366350908 · doi:10.1097/jom.0000000000002867

Traffic-Related Air Pollution and Risk of Lung, Breast, and Urinary Tract Cancer in Halifax, Nova Scotia

2023· article· en· W4366350908 on OpenAlexaffabout
Trevor Dummer, Yu Xing, Yunsong Cui, Laura Nauta, Nathalie Saint‐Jacques, Molly Sweeney‐Magee, Daniel Rainham

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsDalhousie UniversityNova Scotia Health AuthorityUniversity of British ColumbiaCapital District Health Authority
Fundersnot available
KeywordsBreast cancerLung cancerMedicineUrinary systemIncidence (geometry)Risk factorNova scotiaLogistic regressionCancerEnvironmental healthOncologyCase-control studyInternal medicineGynecologyGeography

Abstract

fetched live from OpenAlex

OBJECTIVES: We assessed the association of traffic-related air pollution (TRAP) with the incidence of lung, breast, and urinary tract cancer in Halifax, Nova Scotia. METHODS: Our case-control study included 2315 cancers and 8501 age-sex-matched controls. Land-use regression was used to estimate TRAP concentrations. Logistic regression was used to assess cancer risk in relation to TRAP, adjusting for community social and material deprivation. RESULTS: There was no association between the risk of lung, breast, or urinary tract cancer in relation to TRAP. Lung cancer risk was significantly increased in the most deprived communities, whereas breast cancer risk was highest in the least deprived communities. CONCLUSIONS: In a city characterized by low levels of ambient air pollution, there was no evidence of a linear increased lung, breast, or urinary tract cancer risk in relation to TRAP.

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)0.001
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 score0.152
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.315
Teacher spread0.290 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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