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The role of exercise in managing the adverse effects of androgen deprivation therapy in men with prostate cancer

2011· article· en· W1970657768 on OpenAlexaff
Robyn M. Murphy, Richard J. Wassersug, Gail Dechman

Bibliographic record

VenuePhysical Therapy Reviews · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineProstate cancerAndrogen deprivation therapyAerobic exerciseAdverse effectQuality of life (healthcare)Physical therapyExercise prescriptionMEDLINEMedical prescriptionResistance trainingCancerInternal medicine

Abstract

fetched live from OpenAlex

Background: Androgen deprivation therapy (ADT) is used in the treatment of prostate cancer; however, the side effects of this therapy can be detrimental to a man’s physical and mental health. There has been a growing interest in exercise as a management strategy for many of these problems.Objectives: The aim of this paper is to critically review the ways that resistance and aerobic exercise can ameliorate the adverse effects of ADT in men with prostate cancer. We also provide guidelines for exercise prescription and suggestions to improve adherence.Methods: Studies investigating the effectiveness of exercise for men receiving ADT were reviewed. The online MEDLINE database was searched systematically using appropriate keywords. Citation tracking from these retrieved papers was also used.Major findings: Moderate to high intensity exercise has been shown to reduce the negative impact of many of the side effects of ADT such as muscle loss, decreased strength, fatigue, decreased functional performance, and impaired quality of life.Conclusion: Exercise should be viewed as a primary way to combat the detrimental effects of ADT. Men starting or already undergoing this treatment should be given guidelines for resistance and aerobic exercises.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.208

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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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
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

Citations7
Published2011
Admission routes1
Has abstractyes

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