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Record W4214857569 · doi:10.1080/01635581.2022.2047738

Dietary Fiber, Insulin and Breast Tissue Composition at Age 15–18: A Cross-Sectional Study

2022· article· en· W4214857569 on OpenAlexafffund
Lisa J. Martin, Sudipta Saha, Linda Linton, Monica Taylor, Jie Zhu, Sofia Chavez, Greg J. Stanisz, Salomon Minkin, Norman F. Boyd

Bibliographic record

VenueNutrition and Cancer · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsSunnybrook HospitalPrincess Margaret Cancer CentreUniversity Health Network
FundersNational Institutes of HealthCanadian Breast Cancer Research AlliancePrincess Margaret Cancer FoundationMinistry of Health, Ontario
KeywordsBreast cancerMedicineCross-sectional studyInsulinEndocrinologyInternal medicinePhysiologyWaistHormoneDietary fiberObesityCancerFood scienceBiologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Risk of breast cancer in adult life is influenced by body size and height in childhood, but the mechanisms responsible for these associations are currently unknown. We carried out research to determine if, at age 15-18, measures of dietary intake were associated with body size, hormones, and with variations in breast tissue composition that in adult life are associated with risk of breast cancer. METHODS: In a cross-sectional study of 766 healthy Caucasian women aged 15-18, we measured percent breast water (PBW), total breast water and fat by magnetic resonance (MR), and assessed dietary intake using a validated food frequency questionnaire. We also measured height, weight, skin-fold thicknesses and waist-to-hip ratio, and in fasting blood assayed glucose and insulin. RESULTS: After adjustment for age, measures of body size, and energy intake, dietary fiber (insoluble and total fiber) and insulin were associated positively and significantly with PBW. CONCLUSIONS: Dietary fiber and fasting insulin were associated with breast tissue measures. These data suggest a potential approach to breast cancer prevention.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.996

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)0.0050.000

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 head0.035
GPT teacher head0.343
Teacher spread0.308 · 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.

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
Published2022
Admission routes2
Has abstractyes

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