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Record W4408140758 · doi:10.1093/eurheartj/ehaf100

Cardio-oncology rehabilitation and exercise: evidence, priorities, and research standards from the ICOS-CORE working group

2025· review· en· W4408140758 on OpenAlexaff
Scott C. Adams, Fernando Rivera-Theurel, Jessica M. Scott, Michelle B. Nadler, Stephen Foulkes, Darryl P. Leong, Tormod S. Nilsen, Charles Porter, Mark J. Haykowsky, Husam Abdel‐Qadir, Sarah C. Hull, Neil M. Iyengar, Christina M. Dieli‐Conwright, Susan Dent, Erin J. Howden

Bibliographic record

VenueEuropean Heart Journal · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsWomen's College HospitalMcMaster UniversityPopulation Health Research InstituteUniversity of AlbertaPrincess Margaret Cancer CentreToronto Rehabilitation InstituteHamilton Health SciencesUniversity of TorontoUniversity Health Network
FundersNational Cancer Institute
KeywordsMedicineRehabilitationMultidisciplinary approachCore (optical fiber)Physical therapy

Abstract

fetched live from OpenAlex

The aim of this whitepaper is to review the current state of the literature on the effects of cardio-oncology rehabilitation and exercise (CORE) programmes and provide a roadmap for improving the evidence-based to support the implementation of CORE. There is an urgent need to reinforce and extend the evidence informing the cardiovascular care of cancer survivors. CORE is an attractive model that is potentially scalable to improve the cardiovascular health of cancer survivors as it leverages many of the existing frameworks developed through decades of delivery of cardiac rehabilitation. However, there are several challenges within this burgeoning field, including limited evidence of the efficacy of this approach in patients with cancer. In this paper, a multidisciplinary team of international experts highlights priorities for future research in this field and recommends standards for the conduct of research.

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)0.070
metaresearch head score (Gemma)0.113
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.930
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.113
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0100.007
Science and technology studies0.0010.002
Scholarly communication0.0070.005
Open science0.0040.006
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.227
GPT teacher head0.511
Teacher spread0.284 · 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.

Study designNot applicable
DomainMethods
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

Citations25
Published2025
Admission routes1
Has abstractyes

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