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Record W4411236856 · doi:10.2215/cjn.0000000784

Physical Activity and Exercise for Cardiometabolic Health and Fitness in CKD

2025· article· en· W4411236856 on OpenAlexaff
Brandon M. Kistler, Danielle L. Kirkman, Dave Kusni, Geovana Martín-Alemañy, Heitor S. Ribeiro, Brett Tarca, Stephanie Thompson, João L. Viana, Thomas J. Wilkinson, Ken Wilund

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsUniversity of Alberta
FundersNational Center for Advancing Translational Sciences
KeywordsMedicineKidney diseaseDiseasePhysical activityPhysical therapyPhysical fitnessInternal medicine

Abstract

fetched live from OpenAlex

People with CKD have a high cardiovascular (CV) disease burden. Physical activity and exercise can improve CV risk, but adaptations are specific to the activity performed. Therefore, changes in individual CV risk factors may be influenced by variables such as the volume and type of exercise. This narrative review will outline the evidence for the effects of physical activity and exercise type on cardiometabolic risk factors in adults and provide insights for patients and clinicians. Current evidence suggests that changes in risk factors such as cardiorespiratory fitness and body composition demonstrate specificity to exercise type across the CKD spectrum. However, limited data for each exercise type within some subgroups ( e.g ., disease stage), trial heterogeneity, and other barriers limit the ability to draw definitive conclusions regarding optimal exercise type for some outcomes. Despite these gaps, evidence supports physical activity and exercise's role in improving CV health in people with CKD. A greater emphasis on activity counseling, multifactorial interventions, and implementation strategies may help to maximize the effects of physical activity and exercise on CV health in people with CKD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.538
Teacher spread0.448 · 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 teacher head, 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

Citations3
Published2025
Admission routes1
Has abstractyes

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