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Record W7161801952 · doi:10.82308/9054

The association between the incidence of postmenopausal breast cancer and occupational exposure to selected organic solvents in Montreal

2023· dissertation· en· W7161801952 on OpenAlexaboutno aff
Sydney Westra

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerOdds ratioIncidence (geometry)EpidemiologyCohort studyOccupational exposureConfidence intervalLogistic regressionOccupational medicine

Abstract

fetched live from OpenAlex

Introduction: Breast cancer is the most diagnosed cancer among women and accepted risk factors explain 25% to 47% of cases. Organic solvents are used widely in the workplace. According to a hypothesis postulated in the 1990s, exposure to organic solvents may increase the risk of developing breast cancer, yet there is insufficient data to confirm this hypothesis. The objective of my thesis was to determine whether past occupational exposures to selected organic solvents were associated with the incidence of invasive breast cancer in postmenopausal women in Montréal.Materials and Methods: To meet this objective, I first undertook a structured review of the peer-reviewed case-control and cohort studies that were used to investigate breast cancer and exposure to selected organic solvents that produce reactive metabolites when metabolized in the body. I used SCOPUS, MEDLINE (Ovid) and Web of Science databases to identify epidemiological studies that estimated associations between the risk of developing or dying from malignant breast cancer and past exposure to selected organic solvents with reactive metabolites. Second, I analyzed occupational data from a population-based case-control study (2008 to 2011) that elicited from participants using in-depth interviews information on risk factors for breast cancer as well as details of each job they had during their lifetime. A team of industrial hygienists and chemists translated each detailed job description into specific chemical and physical exposures. I selected six individual solvents and four groups of solvents. Unconditional logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for metrics of past exposures to the selected solvents. Metrics of exposure included any previous exposure, average frequency in hours per week, duration in years, and average cumulative concentration with concentration on a scale of 1 (“low”), 2 (“medium”), 3 (“high”) weighted by hours per work week exposed.Results: I identified 32 papers to include in the review and presented the findings by type of solvent. In the case-control study, 695 cases and 608 controls were enrolled and after adjusting for potential confounding I found increased ORs for average cumulative concentration of exposure to mononuclear aromatic hydrocarbons (OR: 1.52, 95%CI: 1.04, 2.28), chlorinated alkanes (OR: 2.42, 95%CI: 1.23, 5.68), toluene (OR: 1.59, 95%CI: 1.02, 2.59), and a group of organic solvents with reactive metabolites (OR: 1.53, 95%CI: 1.08, 2.24). Positive associations were found across all metrics of exposure and were higher among women who had estrogen positive/progesterone negative tumours. Conclusion: In my review of the literature, I did not find sufficient evidence to determine whether any of the selected organic solvents are implicated in the etiology of postmenopausal breast cancer My results from the population-based case-control study suggest that occupational exposure to certain organic solvents may increase the risk of incident postmenopausal 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 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)0.004
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 score0.140
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.274
Teacher spread0.268 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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