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Record W2024459972 · doi:10.1007/s13668-013-0069-8

The Influence of Energetic Factors on Biomarkers of Postmenopausal Breast Cancer Risk

2013· review· en· W2024459972 on OpenAlexafffund
Heather K. Neilson, Shannon M. Conroy, Christine M. Friedenreich

Bibliographic record

VenueCurrent Nutrition Reports · 2013
Typereview
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsUniversity of CalgaryAlberta Health Services
FundersAlberta InnovatesAlberta Cancer Foundation
KeywordsMedicineBreast cancerBiomarkerInternal medicineOncologyOverweightCancerClinical nutritionRandomized controlled trialInsulin resistancePhysiologyWeight lossBioinformaticsPostmenopausal womenEndocrinologyObesityBiology

Abstract

fetched live from OpenAlex

Strong and consistent evidence exists that physical activity reduces breast cancer risk by 10-25 %, and several proposed biologic mechanisms have now been investigated in randomized, controlled, exercise intervention trials. Leading hypothesized mechanisms relating to postmenopausal breast cancer include adiposity, endogenous sex hormones, insulin resistance, and chronic low-grade inflammation. In addition, other pathways are emerging as potentially important, including those involving oxidative stress and telomere length, global DNA hypomethylation, immune function, and vitamin D exposure. Recent exercise trials in overweight/obese postmenopausal women implicate weight loss as a mechanism whereby exercise induces favorable changes in circulating estradiol levels and other biomarkers as well. Still it is plausible that some exercise-induced biomarker changes do not require loss of body fat, whereas others depend on abdominal fat loss. We highlight the latest findings from randomized, controlled trials of healthy postmenopausal women, relating exercise to proposed biomarkers for postmenopausal 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 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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.356
Teacher spread0.322 · 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 designNot applicable
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

Citations67
Published2013
Admission routes2
Has abstractyes

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