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Exercise and the Heart Failure Patient: Aerobic vs Strength Training—Is There a Need for Both?

2006· review· en· W2055389079 on OpenAlexaff
Elizabeth Gunn, Kelly M. Smith, Robert S. McKelvie, Heather M. Arthur

Bibliographic record

VenueProgress in Cardiovascular Nursing · 2006
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAerobic exerciseAerobic capacityQuality of life (healthcare)Strength trainingHeart failurePhysical therapyWeaknessPhysical medicine and rehabilitationActivities of daily livingExercise intolerancePhysical strengthMuscle weaknessAtrophyMuscle strengthMuscle atrophyRehabilitationCardiologyInternal medicineNursingSurgery

Abstract

fetched live from OpenAlex

Heart failure (HF) is characterized by general muscular weakness, muscle atrophy, and exercise intolerance that lead to reductions in functional capacity, ability to perform activities of daily living, and health-related quality of life. Until recently, exercise programs for patients with HF were centered on aerobic exercise training alone. Although many activities of daily living require significant muscle strength, the role of strength training for HF patients, either alone or in combination with aerobic exercise, has not been well studied. There is suggestive evidence that combined strength-aerobic exercise training may offer additional benefits in terms of health-related quality of life and functional capacity. Strength training can be targeted to reduce muscle atrophy to a greater extent than aerobic training. Further research is required to isolate the specific role of strength training regarding improvements in prognosis, HF-related morbidity and hospitalization, and health-related quality of life for patients with HF.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.025
GPT teacher head0.303
Teacher spread0.277 · 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.

Study designOther design
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

Citations18
Published2006
Admission routes1
Has abstractyes

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