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The multidisciplinary community of exercise oncology practice: current status and future directions

2025· article· en· W4413311567 on OpenAlexaff
Chao Cao, Kristin L. Campbell, Allison Betof Warner, Nancy Campbell, Anna L. Schwartz, Christine Cleary, Karen Y. Wonders, L Capozzi, Jennifer A. Ligibel

Bibliographic record

VenueJNCI Monographs · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
FundersNational Center for Research ResourcesNational Institute of Nursing ResearchNational Cancer InstituteNational Institute on Aging
KeywordsMultidisciplinary approachCurrent (fluid)MedicineMedical educationPsychologyOncologyEngineeringSociologySocial science

Abstract

fetched live from OpenAlex

Exercise is recommended as a part of standard cancer care, based upon its favorable impact on treatment-related side effects and its association with better cancer outcomes. Fully incorporating exercise into oncology practice will require multidisciplinary efforts across oncology and exercise professionals. This article examines current patterns of exercise advice and prescription in oncology settings and highlights the roles of oncology clinicians, physiatrists, physical and occupational therapists, exercise physiologists and fitness trainers, and patient advocates in expanding exercise oncology across the cancer continuum. Future efforts to enhance provider education, expand community-based programs, establish referral pathways, and address policy challenges related to reimbursement will be needed to establish exercise as a universally accessible and effective component of oncology care.

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)0.022
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0040.004
Scholarly communication0.0090.009
Open science0.0030.009
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0150.002

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 head0.019
GPT teacher head0.363
Teacher spread0.344 · 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 designNot applicable
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

Citations2
Published2025
Admission routes1
Has abstractyes

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