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Record W2864030818 · doi:10.1097/cpt.0000000000000096

Pathophysiology of Muscle in Pulmonary and Cardiovascular Conditions

2018· article· en· W2864030818 on OpenAlexaff
Karina Tamy Kasawara, Maria Miñana Castellanos, Masatoshi Hanada, W. Darlene Reid

Bibliographic record

VenueCardiopulmonary Physical Therapy Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsMedicinePathophysiologyMuscle weaknessCardiologyHeart failureDiseasePulmonary function testingWeaknessSkeletal muscleMuscle atrophyIntensive care medicineEtiologyAtrophyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Purpose: To provide an overview of skeletal muscle pathophysiology in pulmonary and cardiovascular conditions commonly managed by physical therapists. Summary of Key Points: This review describes the muscle pathophysiology associated with congestive heart failure, chronic obstructive pulmonary disease, interstitial lung disease, cystic fibrosis, intensive care unit–acquired weakness, immobilization, and aging. Causes of poor muscle performance are multifactorial; disease-specific and generic factors can contribute to the etiology. The time course of deterioration of peripheral and ventilatory muscle may each follow a distinctive course dependent on disease severity, its progression, and other influencing factors. Generic factors that are common in many respiratory and cardiovascular conditions are systemic inflammation and oxidative stress leading to peripheral and ventilatory muscle dysfunction that is accentuated by reduced physical activity. Loss of muscle function associated with aging is also reflected in both peripheral and ventilatory muscles. Evidence of how exercise training can counter the deleterious effects of disease on physical function is outlined. Statement of Conclusions: Patients with pulmonary and cardiovascular conditions may experience atrophy and weakness due to macroscopic, cellular, and metabolic alterations. Physical therapy interventions to improve muscle function need to consider the potential reversibility and related time course of the underlying pathophysiology of muscle dysfunction.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.929
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.020
GPT teacher head0.298
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.

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

Citations9
Published2018
Admission routes1
Has abstractyes

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