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Record W2008542909 · doi:10.1097/jom.0b013e3182728eab

Risk of Selected Cancers due to Occupational Exposure to Chlorinated Solvents in a Case–Control Study in Montreal

2012· article· en· W2008542909 on OpenAlexafffundabout
Krista Christensen, David Vizcaya, Harriet Richardson, Jérôme Lavoué, Kristan J. Aronson, Jack Siemiatycki

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsTrichloroethyleneMedicineOdds ratioCase-control studyProstate cancerCancerEnvironmental healthPopulationOccupational exposureSolvent exposureInternal medicineToxicologyOncologyEnvironmental chemistryChemistryBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the association between exposure to chlorinated solvents and cancer. METHODS: We conducted a case-control study of occupational exposures and cancer in Montreal, Quebec, Canada, including 3730 cancer cases and 533 population controls. Occupational exposures were derived using a combination of subject-reported job history and expert assessment. We examined the associations between two chemical families and six chlorinated solvents with 11 sites of cancer. RESULTS: The majority of the associations examined were null, although many were based on small numbers. We found two significantly elevated odds ratios (ORs), one between perchloroethylene and prostate cancer (OR = 4.3; 95% CI: 1.4 to 13) and another between trichloroethylene and melanoma (OR = 3.2; 95% CI: 1.0 to 9.9). CONCLUSIONS: There was little evidence of associations between chlorinated solvents and cancer. Limited power precludes strong inferences about absence of risk. We raise hypotheses about two possible associations: perchloroethylene with prostate cancer and trichloroethylene with melanoma.

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 score0.071
Threshold uncertainty score0.345

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 head0.011
GPT teacher head0.275
Teacher spread0.264 · 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

Citations33
Published2012
Admission routes3
Has abstractyes

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