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Record W2043725265 · doi:10.1139/h04-018

Mechanisms and Management of Fatigue in Health and Disease: Symposium Introduction

2004· review· en· W2043725265 on OpenAlexaff
Howard J. Green

Bibliographic record

VenueCanadian Journal of Applied Physiology · 2004
Typereview
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDiseaseMedicineGerontologyPsychologyPathology

Abstract

fetched live from OpenAlex

Exercise intolerance is a condition commonly experienced by both the healthy and those with disease. Yet we have only a limited understanding of the underlying mechanisms and, consequently, the management of this condition. In this Symposium, a major objective was to address the role of the muscle cell in weakness and fatigue. We have focused on addressing the advances made in characterizing the basis of muscle cell contractility with particular respect to the processes and proteins involved in excitation and contraction, and how these processes can be modified during repetitive activity. Three reviews are provided on this subject. Each addresses a specific link in the cascade of events from neural activation of the muscle to the generation of force. In the first review the processes involved in signal transduction in the sarcolemma and T-tubule, and which regulate membrane excitability, are examined. The second review analyzes the sarcoplasmic reticulum regulation of the intracellular messenger that controls the myofibrillar complex, namely free calcium. The final review in this series deals with the events regulating actin-myosin behaviour and the mechanical response. All reviews place special emphasis on how different sites can be modified by repetitive activity and, as a consequence, how they can represent a potential source of fatigue. Since it is important to understand the nature, manifestations, and measurement of weakness and fatigue, a comprehensive review on these topics is also provided.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.683
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.028
GPT teacher head0.310
Teacher spread0.282 · 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 designSystematic review
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

Citations5
Published2004
Admission routes1
Has abstractyes

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