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Record W2965176010 · doi:10.3390/nu11081769

Whole Grain Consumption for the Prevention and Treatment of Breast Cancer

2019· review· en· W2965176010 on OpenAlexaff
Mingsi Xie, Jie Liu, Rong Tsao, Ziyuan Wang, Baoguo Sun, Jing Wang

Bibliographic record

VenueNutrients · 2019
Typereview
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Natural Science Foundation of China
KeywordsBreast cancerWhole grainsNutraceuticalMedicineCancerEpidemiologyEnvironmental healthOncologyBiotechnologyInternal medicineFood scienceBiologyPathology

Abstract

fetched live from OpenAlex

Breast cancer is one of the most common and malignant cancers among females worldwide. Several epidemiological studies have indicated the inverse correlation between the intake of whole grains and the incidence of breast cancer. Whole grains are the most fundamental and important food source of bioactive phytochemicals, which have well-defined roles in the management of each stage of breast carcinogenesis. To better understand the value of whole grains in future prevention and treatment of breast cancer, the effects and possible mechanisms of six different whole grain cereals, which are the most commonly consumed throughout the world, are introduced in the current review. Moreover, the bioactive compounds extracted from whole grains are adequately formulated and the underlying mechanism of action is illustrated. In addition, the present limitations and future perspective of whole grain consumption for breast cancer are also concluded. The objective of this review is to promote the development of nutraceutical and functional food from whole grains and its application for reducing the risk of 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 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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.322

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 head0.106
GPT teacher head0.372
Teacher spread0.266 · 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 designOther design
Domainnot available
GenreReview

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

Citations61
Published2019
Admission routes1
Has abstractyes

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