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Record W2943953946 · doi:10.1139/apnm-2018-0862

Understanding exercise-dependent plasticity of motoneurons using intracellular and intramuscular approaches

2019· review· en· W2943953946 on OpenAlexaffvenue
Duane C. Button, Jayne M. Kalmar

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2019
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsWilfrid Laurier UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsNeurosciencePlasticityNeuroplasticitySpinal cord injuryMotor unitIntracellularSpinal cordBiologyPhysical medicine and rehabilitationPsychologyMedicinePhysics

Abstract

fetched live from OpenAlex

Spinal motoneurons (MN) exhibit exercise-dependent adaptations to increased activity, such as exercise and locomotion, as well as decreased activity associated with disuse, spinal cord injury, and aging. The development of several experimental approaches, in both human and animal models, has contributed significantly to our understanding of this plasticity. The purpose of this review is to summarize how intracellular recordings in an animal model and motor unit recordings in a human model have, together, contributed to our current understanding of exercise-dependent MN plasticity. These approaches and techniques will allow neuroscientists to continue to advance our understanding of MN physiology and the plasticity of the "final common path" of the motor system, and to design experiments to answer the critical questions that are emerging in this field.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.288
GPT teacher head0.367
Teacher spread0.079 · 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 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

Citations10
Published2019
Admission routes2
Has abstractyes

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