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Why and How Limb Muscle Mass and Function Should Be Measured in Patients with Chronic Obstructive Pulmonary Disease

2015· review· en· W1790637762 on OpenAlexafffund
Didier Saey, François Maltais

Bibliographic record

VenueAnnals of the American Thoracic Society · 2015
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health Research
KeywordsMedicineCOPDIsometric exercisePhysical medicine and rehabilitationBioelectrical impedance analysisPhysical therapyMuscle massCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Impaired limb muscle function is a common occurrence in patients with chronic obstructive pulmonary disease (COPD), and it negatively influences exercise tolerance, quality of life, and even survival. Assessment of limb muscle mass and function in COPD is highly encouraged; it should include the quadriceps muscle, but other lower and upper limb muscles may also be evaluated to provide valuable information. Quantification of muscle mass as well as assessment of muscle strength and endurance are suggested. Bioelectrical impedance and dual-energy X-ray absorption can be realistically used in the clinical environment to monitor body composition. Although sophisticated computerized dynamometers provide the most accurate assessment, simple exercise and testing equipment are valid alternatives and they should help in implementing limb muscle function assessment in clinical settings. Isometric measurements, using strain-gauges or hand-held dynamometers, should be favored for their simplicity, availability, and quality of information provided. This perspective provides a rationale for the evaluation of limb muscle mass and function in COPD in routine clinical practice. In addition, measurement techniques used to assess limb muscle mass, strength, endurance, and fatigue in various clinical settings are discussed.

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 score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.114
GPT teacher head0.383
Teacher spread0.269 · 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

Citations69
Published2015
Admission routes2
Has abstractyes

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