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Breast Cancer Risk in Relation to Alcohol Consumption and BRCA Gene Mutations - A Case-Only Study of Gene-Environment Interaction

2011· article· en· W1939759378 on OpenAlexafffundabout
Jessica Dennis, Daniel Krewski, Frédérique-Sophie Côté, Eve Fafard, Julian Little, Parviz Ghadirian

Bibliographic record

VenueThe Breast Journal · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversity of Ottawa
FundersNational Cancer InstituteCanadian Breast Cancer Research AllianceBreast Cancer Alliance
KeywordsMedicineBreast cancerBRCA mutationOdds ratioOncologyGermline mutationAlcohol consumptionGeneticsMutationCancerPenetranceInternal medicineGeneAlcoholPhenotypeBiology

Abstract

fetched live from OpenAlex

The variable penetrance of the BRCA1 and BRCA2 genes suggests that other genetic or environmental factors may interact with these mutations to modify breast cancer risk. The objective of this study was to measure departures from multiplicative effects of alcohol consumption and BRCA gene mutations. A cohort of French-Canadian breast cancer patients was tested for BRCA gene mutations and completed a food frequency questionnaire. The case-only odds ratio (COR) was calculated. A total of 857 women, including 10 BRCA1 and 33 BRCA2 mutation carriers, participated in the study. No significant interaction between alcohol consumption and BRCA1 mutations was detected, although the interaction with wine consumption suggested a sub-multiplicative effect (COR = 0.38, 95% CI: 0.08-1.81). Consumption of alcohol other than wine interacted significantly with BRCA2 mutations (COR = 2.15, 95% CI: 1.03-4.49). Consumption of wine may protect against BRCA1-associated tumors, while women with BRCA2 mutations may be at greater risk of alcohol-induced 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)0.003
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 score0.130
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.293
Teacher spread0.265 · 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

Citations23
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueThe Breast JournalSame topicBRCA gene mutations in cancerFrench-language works237,207