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Record W2071190091 · doi:10.1080/01635581.2011.563026

Food, Beverage, and Macronutrient Intakes in Postmenopausal Caucasian and Chinese-Canadian Women

2011· article· en· W2071190091 on OpenAlexaffabout
Carolyn Tam, Gregory Hislop, Anthony J. Hanley, Salomon Minkin, Norman F. Boyd, Lisa Martin

Bibliographic record

VenueNutrition and Cancer · 2011
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsBreast cancerMedicineEnvironmental healthEthnic groupPostmenopausal womenRisk factorChinaDemographyCancerGerontologyEndocrinologyInternal medicineGeography

Abstract

fetched live from OpenAlex

International differences in breast cancer rates and diet, and studies in migrants, suggest that diet may be a modifiable risk factor for breast cancer. The goal of this cross-sectional study was to examine the dietary intakes of women from populations considered to be at different risks for breast cancer. We collected four 24-h food recalls in 3 groups of postmenopausal Canadian women: Caucasians (n = 392), Chinese women born in the West or who migrated to the West before age 21 (n = 156), and recent Chinese migrants (n = 383). Compared to Caucasians, recent Chinese migrants had lower energy and fat intakes and higher protein and carbohydrate intakes. Recent Chinese migrants consumed higher amounts of grains, vegetables, fish, and soy and lower amounts of alcohol, meat, dairy products, and sweets than Caucasians. Western-born Chinese and early Chinese migrants had intakes intermediate between the other 2 groups. The differences in intake between the ethnic groups suggest foods and nutrients that may contribute to the differences in risk of breast cancer between women in Canada and China. Future work will examine whether these dietary differences are associated with biological markers of breast cancer risk.

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 score0.032
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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

Citations8
Published2011
Admission routes2
Has abstractyes

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