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Record W2474851916 · doi:10.1158/1055-9965.epi-16-0223

Exercise and Prostate Cancer: Evidence and Proposed Mechanisms for Disease Modification

2016· review· en· W2474851916 on OpenAlexaff
Brian Hayes, Lauren Brady, Michaël Pollak, Stephen P. Finn

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2016
Typereview
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsMcGill UniversityTrinity College
FundersWorld Cancer Research Fund
KeywordsProstate cancerCancerMedicineProinflammatory cytokineMyokineDiseaseAdipokineOncologyImmunologyObesityInternal medicineBioinformaticsInflammationInsulin resistanceBiology

Abstract

fetched live from OpenAlex

Exercise has many potential benefits in relation to cancer. Apart from primary prevention, these include improvement of nonspecific cancer-related symptoms, amelioration of symptoms and cardiovascular risk factors related to cancer treatment, and improvements in various quality-of-life-related factors. Increasing evidence also points toward improved cancer-free and overall survival in cancer patients who undertake regular exercise, findings which should encourage further research in this area. Obesity is known to be associated with a proinflammatory, prothrombotic humoral milieu, which may promote aggressiveness in prostate cancer through interactions with NK-cell-mediated killing of circulating tumor cells, through platelet-circulating tumor cell interactions, and through alterations in adipokine and myokine profile among others. Physical activity reduces levels of systemic inflammatory mediators and so exercise may represent an accessible and cost-effective means of ameliorating the proinflammatory effects of obesity in cancer patients. This review outlines the evidence for the benefits of exercise in these patients, focusing on prostate cancer, and delineates current theories of the underlying biological mechanisms. Cancer Epidemiol Biomarkers Prev; 25(9); 1281-8. ©2016 AACR.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.155
GPT teacher head0.455
Teacher spread0.300 · 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 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

Citations34
Published2016
Admission routes1
Has abstractyes

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