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Record W2904623098 · doi:10.1136/oemed-2018-105261

Women’s occupational exposure to polycyclic aromatic hydrocarbons and risk of breast cancer

2018· article· en· W2904623098 on OpenAlexafffundabout
Derrick G. Lee, Igor Burstyn, Agnes S. Lai, Anne Grundy, Melissa C. Friesen, Kristan J. Aronson, John J. Spinelli

Bibliographic record

VenueOccupational and Environmental Medicine · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsUniversity of British ColumbiaQueen's UniversityUniversité de MontréalSt. Francis Xavier University
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsBreast cancerMedicineJob-exposure matrixCancerOccupational exposureFamily historyEnvironmental healthOccupational medicineOccupational cancerInternal medicine

Abstract

fetched live from OpenAlex

Objective To estimate the association between occupational polycyclic aromatic hydrocarbon (PAH) exposure and female breast cancer. Methods Lifetime work histories for 1130 cases and 1169 controls from British Columbia and Ontario (Canada) were assessed for PAH exposure using a job-exposure matrix based on compliance measurements obtained during US Occupational Safety and Health Administration workplace safety inspections. Results Exposure to any level of PAHs was associated with an increased risk of breast cancer (OR=1.32, 95% CI: 1.10 to 1.59), as was duration at high PAH exposure (for >7.4 years: OR=1.45, 95% CI: 1.10 to 1.91; p trend =0.01), compared with women who were never exposed. Increased risk of breast cancer was most strongly associated with prolonged duration at high occupational PAH exposure among women with a family history of breast cancer (for >7.4 years: OR=2.79, 95% CI: 1.25 to 6.24; p trend <0.01). Conclusions Our study suggests that prolonged occupational exposure to PAH may increase breast cancer risk, especially among women with a family history of breast cancer.

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)0.000
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 score0.442
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.243
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 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

Citations58
Published2018
Admission routes3
Has abstractyes

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