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Record W4324137690 · doi:10.1136/oem-2023-epicoh.200

O-57 Occupational exposure to endocrine disruptors and colorectal cancer risk in two Canadian cohorts

2023· article· en· W4324137690 on OpenAlexaffabout
Laura Pelland-St-Pierre, Marc-André Verner, Vikki Ho

Bibliographic record

VenueAbstracts · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
Fundersnot available
KeywordsMedicineColorectal cancerOdds ratioJob-exposure matrixConfoundingCohortVitamin D and neurologyInternal medicineRecord linkageConfidence intervalCohort studyLogistic regressionCancerPopulationSubclinical infectionEnvironmental healthOncologyPhysiology

Abstract

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Introduction Sex hormones have been implicated in the etiology of colorectal cancer. Endocrine disruptors (EDCs) are compounds that can interfere with sex hormone signalling and cause adverse health effects, including cancer. Exposure to EDCs is ubiquitous, but exposure in some workplaces occurs at much higher levels than in the general population. Objective To determine whether occupational exposure to EDCs is associated with colorectal cancer risk. Material and Methods A case-cohort study was nested in the Alberta’s Tomorrow Project (ATP) and in the Ontario Health Study (OHS). Incident cases of colorectal cancer were identified (NATP=202, NOHS=605); a sub-cohort of 3,464 participants was selected at baseline (NATP=565, NOHS=2,899). Occupational exposure to 17 EDCs was estimated via linkage to CANJEM, a job-exposure matrix, for participants’ longest-held job. Specifically, CANJEM provides a frequency-weighted intensity metric of exposure and it was used to categorize participants into never exposed, exposed and substantially exposed to each individual EDC. Multivariable logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for colorectal cancer associated with occupational exposure to EDCs while controlling for confounders identified using a directed acyclic graph. Results In ATP, exposure to arsenic (OR=2.86, 95%CI: 1.06–7.63), copper (OR=0.53, 95%CI: 0.29–0.92), lead (OR=0.58, 95%CI: 0.34–0.97) and substantial exposure to arsenic (OR=2.87, 95%CI: 1.01–1.80), phenol (OR=0.25, 95%CI: 0.08–0.61), and trichloroethylene (OR=0.45, 95%CI: 0.21–0.90) were associated with colorectal cancer. In OHS, exposure to polychlorinated biphenyls (OR=3.95, 95%CI: 1.82–8.55), styrene (OR=0.47, 95%CI: 0.26–0.79), and substantial exposure to aluminum (OR=1.32, 95%CI: 1.03–1.68), cadmium (OR=0.59, 95%CI: 0.38–0.87), lead (OR=1.29, 95%CI: 1.03–1.60), phthalates (OR=0.52, 95%CI: 0.25–0.96), and trichloroethylene (OR=1.43, 95%CI: 1.08–1.88) were associated with colorectal cancer. Conclusion Of the 17 EDCs, five were associated with an increased risk, and seven with a decreased colorectal cancer risk; however, none of the associations were consistent between the two cohorts.

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)0.001
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 score0.019
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.295
Teacher spread0.282 · 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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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