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Record W4200410558 · doi:10.51224/cik.v1i3.40

Physical Activity in Adults With Fatigue After Cancer Treatment

2021· article· en· W4200410558 on OpenAlexaff
Rosie Twomey, Samuel T. Yeung, James G. Wrightson, Lillian Sung, Paula D. Robinson, Guillaume Y. Millet, S. Nicole Culos‐Reed

Bibliographic record

VenueCommunications in Kinesiology · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsCalgary Laboratory ServicesAlberta Health ServicesPediatric Oncology GroupAlberta Children's HospitalHospital for Sick ChildrenLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsCancer-related fatiguePsycINFOCINAHLMedicineRandomized controlled trialMEDLINESystematic reviewPhysical therapyPsychological interventionMeta-analysisCancerCochrane LibraryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Physical activity is recommended for the management of cancer-related fatigue (CRF), yet the evidence is primarily based on interventions delivered during cancer treatment, with no eligibility criterion for fatigue. There is a need to examine the quantity and quality of the existing literature on physical activity for clinically-relevant CRF that continues after cancer treatment (post-cancer fatigue). The objective of this systematic review was to summarize and evaluate the effect of physical activity on post-cancer fatigue in adults, using randomized trials where fatigue was an eligibility criterion. Studies were included if they: included adult participants with a cancer diagnosis who had completed initial cancer treatments (e.g., surgery, chemotherapy and/or radiation therapy); explicitly stated that fatigue was a participant eligibility/inclusion criterion, regardless of how this was described or assessed; involved a physical activity intervention; measured fatigue as a primary or secondary outcome. A previous systematic search was updated and electronic databases (Ovid MEDLINE(R), Ovid MEDLINE(R) and In-Process & Other Non-Indexed Citations, Embase, PsycINFO, Cochrane Database of Systematic Reviews, and CINAHL) were last searched on October 13, 2020. The risk of bias was assessed using the Cochrane Collaboration’s tool for randomized trials. A random-effects meta-analysis for the severity of fatigue across different scales at the end of the intervention was conducted. A total of 1035 participants were randomized across 19 studies. We estimate that less than 10% of the randomized trials of physical activity for CRF include people with post-cancer fatigue. The effect of physical activity on post-cancer fatigue was modest and variable (Hedge’s g -0.40; p = 0.010; 95% prediction intervals -1.41 to 0.62). Most studies had an unknown or high risk of bias, there was substantial heterogeneity between trials and evidence for the effect of physical activity on post-cancer fatigue was graded as low certainty. Including people with clinically relevant fatigue is a priority for future research in cancer survivorship. Additional transparently reported randomized clinical trials are needed to better understand the benefits of physical activity for post-cancer fatigue.

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)0.007
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.053
GPT teacher head0.366
Teacher spread0.314 · 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 designObservational
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

Citations4
Published2021
Admission routes1
Has abstractyes

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