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Record W4387710066 · doi:10.1016/j.jaccao.2023.08.008

Incorporating Exercise Training into Cardio-Oncology Care

2023· review· en· W4387710066 on OpenAlexaff
Rebekah L. Wilson, Cami N. Christopher, Eric H. Yang, Ana Barac, Scott C. Adams, Jessica M. Scott, Christina M. Dieli‐Conwright

Bibliographic record

VenueJACC CardioOncology · 2023
Typereview
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsToronto General HospitalTed Rogers Centre for Heart Research
FundersNational Cancer InstituteMemorial Sloan-Kettering Cancer Center
KeywordsMedicineSurvivorship curveIntensive care medicineCancerCancer survivorshipOncologyCancer treatmentInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Cancer treatment-induced cardiotoxicities are an ongoing concern throughout the cancer care continuum from treatment initiation to survivorship. Several "standard-of-care" primary, secondary, and tertiary prevention strategies are available to prevent the development or further progression of cancer treatment-induced cardiotoxicities and their risk factors. Despite exercise's established benefits on the cardiovascular system, it has not been widely adopted as a nonpharmacologic cardioprotective strategy within cardio-oncology care. In this state-of-the-art review, the authors discuss cancer treatment-induced cardiotoxicities, review the existing evidence supporting the role of exercise in preventing and managing these sequelae in at-risk and affected individuals living after cancer diagnoses, and propose considerations for implementing exercise-based services in cardio-oncology practice.

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)0.001
metaresearch head score (Gemma)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.137
GPT teacher head0.411
Teacher spread0.275 · 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
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

Citations34
Published2023
Admission routes1
Has abstractyes

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