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Record W2896229709 · doi:10.1093/jnci/djy161

Targeting Exercise Interventions to Patients With Cancer in Need: An Individual Patient Data Meta-Analysis

2018· review· en· W2896229709 on OpenAlexafffund
Laurien M. Buffart, Maike G. Sweegers, Anne M. May, Mai J. M. Chinapaw, Jonna K. van Vulpen, Robert U. Newton, Daniel A. Galvão, Neil K. Aaronson, Martijn M. Stuiver, Paul B. Jacobsen, Irma M. Verdonck‐de Leeuw, Karen Steindorf, Melinda L. Irwin, Sandra C. Hayes, Kathleen A. Griffith, Alejandro Lucía, F. Herrero, Ilse Mesters, Ellen van Weert, Hans Knoop, Martine M. Goedendorp, Nanette Mutrie, Amanda Daley, Alex McConnachie, Martin Bohus, Lene Thorsen, Karl-Heinz Schulz, Camille E. Short, Erica L. James, Ronald C. Plotnikoff, Martina E. Schmidt, Karin Potthoff, Marc van Beurden, Hester S. A. Oldenburg, Gabe S. Sonke, Wim H. van Harten, Rachel Garrod, Kathryn H. Schmitz, Kerri M. Winters‐Stone, Miranda J. Velthuis, Dennis R. Taaffe, Willem van Mechelen, Marie José Kersten, Frans Nollet, Jennifer Wenzel, Joachim Wiskemann, Johannes Brug, Kerry S. Courneya

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2018
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Alberta
FundersNational Cancer InstituteAmsterdam School of Communication Research, University of AmsterdamKnight Cancer Institute, Oregon Health and Science UniversityUniversitätsklinikum Hamburg-EppendorfUniversitätsklinikum HeidelbergCancer Center AmsterdamVrije Universiteit AmsterdamQueensland University of TechnologyUniversity of AlbertaDeutsches KrebsforschungszentrumUniversiteit AntwerpenRijksuniversiteit GroningenUniversity of TwenteMenzies Centre for Australian Studies, King's College London, University of LondonUniversity of GlasgowYale UniversityAmsterdam University Medical CentersUniversiteit MaastrichtJohns Hopkins UniversityGeorge Washington UniversitySidney Kimmel Comprehensive Cancer CenterUniversiteit van AmsterdamUniversitair Medisch Centrum GroningenEdith Cowan University
KeywordsModerationAerobic exerciseMedicineQuality of life (healthcare)Psychological interventionPhysical therapyRandomized controlled trialPhysical fitnessIntervention (counseling)Physical medicine and rehabilitationInternal medicinePsychology

Abstract

fetched live from OpenAlex

Background: Exercise effects in cancer patients often appear modest, possibly because interventions rarely target patients most in need. This study investigated the moderator effects of baseline values on the exercise outcomes of fatigue, aerobic fitness, muscle strength, quality of life (QoL), and self-reported physical function (PF) in cancer patients during and post-treatment. Methods: Individual patient data from 34 randomized exercise trials (n = 4519) were pooled. Linear mixed-effect models were used to study moderator effects of baseline values on exercise intervention outcomes and to determine whether these moderator effects differed by intervention timing (during vs post-treatment). All statistical tests were two-sided. Results: Moderator effects of baseline fatigue and PF were consistent across intervention timing, with greater effects in patients with worse fatigue (Pinteraction = .05) and worse PF (Pinteraction = .003). Moderator effects of baseline aerobic fitness, muscle strength, and QoL differed by intervention timing. During treatment, effects on aerobic fitness were greater for patients with better baseline aerobic fitness (Pinteraction = .002). Post-treatment, effects on upper (Pinteraction < .001) and lower (Pinteraction = .01) body muscle strength and QoL (Pinteraction < .001) were greater in patients with worse baseline values. Conclusion: Although exercise should be encouraged for most cancer patients during and post-treatments, targeting specific subgroups may be especially beneficial and cost effective. For fatigue and PF, interventions during and post-treatment should target patients with high fatigue and low PF. During treatment, patients experience benefit for muscle strength and QoL regardless of baseline values; however, only patients with low baseline values benefit post-treatment. For aerobic fitness, patients with low baseline values do not appear to benefit from exercise during treatment.

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)0.031
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.050
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0160.054
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.299
GPT teacher head0.451
Teacher spread0.152 · 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 designMeta-analysis
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

Citations105
Published2018
Admission routes2
Has abstractyes

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