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Implementing Cancer Exercise Rehabilitation: An Update on Recommendations for Clinical Practice

2018· article· en· W2885635541 on OpenAlexaff
Kirsten Suderman, Carolyn J. Peddle‐McIntyre, Christopher M. Sellar, Margaret L. McNeely

Bibliographic record

VenueCurrent Cancer Therapy Reviews · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRehabilitationMedicineClinical PracticeCancerPhysical therapyEvidence-based practicePhysical activityAlternative medicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

A growing body of research evidence supports the benefit of exercise for cancer survivors both during and after cancer treatment. The purpose of this paper is to provide an update on our previously published review in 2006 on the state of the evidence supporting exercise for survivors of cancer as well as guidelines for integrating exercise programming in the cancer clinical setting. First, we provide a brief overview on the benefits of exercise as well as preliminary evidence supporting the implementation of community-based exercise programs. Second, we summarize the principles and goals of exercise, and the identified barriers to exercise among cancer survivors. Finally, we propose an interdisciplinary model of care for integrating exercise programming into clinical care including guidelines for medical and pre-exercise screening, exercise testing and programming considerations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.848
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.268
GPT teacher head0.563
Teacher spread0.295 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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