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Record W2810762159 · doi:10.1139/apnm-2018-0229

Exercise biology of neuromuscular disorders

2018· review· en· W2810762159 on OpenAlexafffundvenue
Sean Y. Ng, Alexander Manta, Vladimir Ljubicic

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsSpinal muscular atrophyMyotonic dystrophyDuchenne muscular dystrophyNeuromuscular diseaseMedicinePhysical medicine and rehabilitationSMA*DiseaseMuscular dystrophyNeurosciencePsychologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Neuromuscular disorders (NMDs) are chronic conditions that affect the neuromuscular system. Many NMDs currently have no cure; however, as more effective therapies become available for NMD patients, these individuals will exhibit improved health and/or prolonged lifespans. As a result, persons with NMDs will likely desire to engage in a more diverse variety of activities of daily living, including increased physical activity or exercise. Therefore, there is a need to increase our knowledge of the effects of acute exercise and chronic training on the neuromuscular system in NMD contexts. Here, we discuss the disease mechanisms and exercise biology of Duchenne muscular dystrophy (DMD), spinal muscular atrophy (SMA), and myotonic dystrophy type 1 (DM1), which are among the most prevalent NMDs in children and adults. Evidence from clinical and preclinical studies are reviewed, with emphasis on the functional outcomes of exercise, as well as on the putative cellular mechanisms that drive exercise-induced remodelling of the neuromuscular system. Continued investigation of the molecular mechanisms of exercise adaptation in DMD, SMA, and DM1 will assist in enhancing our understanding of the biology of these most prevalent NMDs. This information may also be useful for guiding the development of novel therapeutic targets for future pursuit.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.277
Teacher spread0.264 · 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 designNot applicable
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

Citations32
Published2018
Admission routes3
Has abstractyes

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