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Record W1784289554 · doi:10.6004/jnccn.2010.0069

Exercise Programs for Cancer-Related Fatigue: Evidence and Clinical Guidelines

2010· review· en· W1784289554 on OpenAlexaff
Margaret L. McNeely, Kerry S. Courneya

Bibliographic record

VenueJournal of the National Comprehensive Cancer Network · 2010
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAerobic exerciseCancer-related fatigueSurvivorship curvePhysical therapyPhysical activityClinical trialResistance trainingProstate cancerPhysical fitnessCancerExercise therapyPhysical exercisePhysical medicine and rehabilitationRandomized controlled trialOncologyInternal medicine

Abstract

fetched live from OpenAlex

This article provides an overview of the research literature and clinical recommendations on aerobic and resistance exercise programs for managing cancer-related fatigue (CRF). Current research evidence suggests that exercise improves CRF, with more recent evidence suggesting greater benefits when exercise programs are administered in the survivorship phase than in the active treatment phase. Moreover, positive changes in fitness from aerobic exercise programs have been shown to mediate the effects of exercise on CRF. Resistance exercise programs show promise in ameliorating CRF, especially in patients with prostate cancer, and should be considered a potential component of the exercise program. Future trials should examine the potential mechanisms through which exercise influences symptoms of CRF and the effect of exercise on CRF in the long-term. More research is needed evaluating exercise programs for cancer survivors with CRF, especially those with moderate-to-severe CRF.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.367
GPT teacher head0.514
Teacher spread0.147 · 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 designSystematic review
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

Citations117
Published2010
Admission routes1
Has abstractyes

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