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Record W2020822068 · doi:10.1002/mus.20541

Assessment of muscle fatigue during exercise in chronic obstructive pulmonary disease

2006· article· en· W2020822068 on OpenAlexaff
Didier Saey, Claude H. Côt́e, M. Jeffery Mador, Louis Laviolette, Pierre Leblanc, Jean Jobin, François Maltais

Bibliographic record

VenueMuscle & Nerve · 2006
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversité LavalCentre hospitalier de l'Université LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsCOPDMedicineMuscle fatigueExercise intoleranceCardiologyPulmonary diseaseElectromyographyInternal medicinePhysical medicine and rehabilitationPhysical therapyHeart failure

Abstract

fetched live from OpenAlex

Contractile fatigue is associated with exercise intolerance in patients with chronic obstructive pulmonary disease (COPD). Contractile fatigue may be assessed by quantifying the decline in strength after a fatiguing protocol but this may pose practical problems. The purpose of this study was to investigate the relationship between the decline in quadriceps strength, quadriceps electrical activity, perception of leg fatigue, and arterial lactate level in patients with COPD during constant work-rate cycling exercise. The decline in quadriceps strength was significantly associated with the decrease in electromyographic median frequency (r = 0.606), leg fatigue perception (r = 0.453), and arterial lactate level (r = 0.384). Using the receiver-operating-characteristic curve, it was found that a 4% decline in electromyographic median frequency had a 94% sensitivity and a 75% specificity to predict contractile fatigue. We conclude that contractile fatigue commonly occurs during cycling exercise in COPD. The electromyographic median frequency appears to be a valuable indirect marker to predict contractile leg fatigue.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.299
Teacher spread0.281 · 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 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

Citations42
Published2006
Admission routes1
Has abstractyes

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