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Record W2605550732 · doi:10.1016/j.envres.2017.03.035

Spatial variations in ambient ultrafine particle concentrations and the risk of incident prostate cancer: A case-control study

2017· article· en· W2605550732 on OpenAlexafffund
Scott Weichenthal, Éric Lavigne, Marie‐France Valois, Marianne Hatzopoulou, Keith Van Ryswyk, Maryam Shekarrizfard, Paul J. Villeneuve, Mark S. Goldberg, Marie‐Élise Parent

Bibliographic record

VenueEnvironmental Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversité du Québec à MontréalArmand Frappier MuseumUniversity of TorontoCarleton UniversityMcGill University Health CentreUniversity of OttawaInstitut National de la Recherche ScientifiqueHealth CanadaMcGill University
FundersFonds de Recherche du Québec - SantéHealth CanadaCancer Research Society
KeywordsProstate cancerUltrafine particleEnvironmental scienceEnvironmental healthParticle (ecology)Risk assessmentCancerMedicineMaterials scienceInternal medicineComputer scienceGeologyNanotechnology

Abstract

fetched live from OpenAlex

BACKGROUND: Diesel exhaust contains large numbers of ultrafine particles (UFPs, <0.1µm) and is a recognized human carcinogen. However, epidemiological studies have yet to evaluate the relationship between UFPs and cancer incidence. METHODS: ) using logistic regression models adjusting for individual-level and ecological covariates. RESULTS: Ambient UFP concentrations were associated with an increased risk of prostate cancer (OR=1.10, 95% CI: 1.01, 1.19) in fully adjusted models when exposures were assigned to residences 10-years prior to diagnosis. This risk estimate increased slightly (OR=1.17, 95% CI; 1.01, 1.35) when modeled as a non-linear natural spline function. A smaller increased risk (OR=1.04, 95% CI: 0.97, 1.11) was observed when exposures were assigned to residences at the time of diagnosis. CONCLUSIONS: Exposure to ambient UFPs may increase the risk of prostate cancer. Future studies are needed to replicate this finding as this is the first study to evaluate this relationship.

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)0.003
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 score0.027
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.053
GPT teacher head0.381
Teacher spread0.328 · 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

Citations47
Published2017
Admission routes2
Has abstractyes

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