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Record W4385979577 · doi:10.1016/j.cjca.2023.08.011

Exercise Rehabilitation for People With End-Stage Kidney Disease: Who Will Fill the Gaps?

2023· review· en· W4385979577 on OpenAlexaffvenue
Stephanie Thompson, Michael K. Stickland, Ken Wilund, Gábor Gyenes, Clara Bohm

Bibliographic record

VenueCanadian Journal of Cardiology · 2023
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of ManitobaOrthopaedic Innovation CentreUniversity of Alberta
Fundersnot available
KeywordsMedicineRehabilitationKidney diseaseQuality of life (healthcare)Physical therapyComorbidityIntensive care medicineDiseaseDialysisStatus quoPhysical medicine and rehabilitationInternal medicineNursing

Abstract

fetched live from OpenAlex

Exercise rehabilitation is a well established therapy for reducing morbidity and mortality and improving quality of life and function across chronic conditions. People with dialysis-dependent kidney failure have a high burden of comorbidity and symptoms, commonly characterised as fatigue, dyspnoea, and the inability to complete daily activities. Despite more than 30 years of exercise research in people with kidney disease and its established benefit in other chronic diseases, exercise programs are rare in kidney care and are not incorporated into routine management at any stage. In this review, we describe the mechanisms contributing to exercise intolerance in those with end-stage kidney disease and outline the role of exercise rehabilitation in addressing the major challenges to kidney care: cardiovascular disease, symptom burden, and physical frailty. We also draw on existing models of exercise rehabilitation from other chronic conditions to inform the way forward and challenge the status quo of exercise rehabilitation in both practice and 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)0.001
metaresearch head score (Gemma)0.003
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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

Citations10
Published2023
Admission routes2
Has abstractno

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Same venueCanadian Journal of Cardiology→Same topicDialysis and Renal Disease Management→French-language works237,207→