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Record W2988878885 · doi:10.21873/anticanres.13828

Dairy Food Consumption and Mammographic Breast Density: The Role of Fat

2019· article· en· W2988878885 on OpenAlex
Elisabeth Canitrot, Caroline Diorio

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnticancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsQuartileMedicineMAMMOGRAPHIC DENSITYFood frequency questionnaireBreast cancerMammographyAnimal scienceLinear regressionFood consumptionEndocrinologyPostmenopausal womenInternal medicineGynecologyCancerConfidence intervalBiologyMathematics

Abstract

fetched live from OpenAlex

Aim: This cross-sectional study aimed to evaluate the associations between low and high-fat dairy food (DF) intake and breast density (BD). Materials and Methods: A total of 775 premenopausal and 771 postmenopausal women recruited during screening mammography completed a food frequency questionnaire. Adjusted linear regression models were used to assess the associations. Results: As frequency quartiles of high-fat DF consumption increased, the adjusted mean of absolute BD increased from 31.5 to 36.1 cm<sup>2</sup> for all women (p<sub>trend</sub>=0.0034) and from 42.4 to 50.1 cm<sup>2</sup> for premenopausal women (p<sub>trend</sub>=0.0047). Conversely, as frequency quartiles of low-fat DF consumption increased, the adjusted mean of absolute BD decreased from 34.7 to 29.6 cm<sup>2</sup> for all women (p<sub>trend</sub>=0.001) and from 49.7 to 40.7 cm<sup>2</sup> for premenopausal women (p<sub>trend</sub>=0.0012). Conclusion: A higher intake of high-fat and low-fat DF is respectively associated with higher and lower BD, particularly in premenopausal women.

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.

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 score0.008
Threshold uncertainty score0.132

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 head0.049
GPT teacher head0.354
Teacher spread0.305 · 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