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Submaximal motor unit firing rates after 8 wk of isometric resistance training

2000· article· en· W2008392804 on OpenAlexaff
Christopher Rich, E. Cafarelli

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2000
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsYork University
Fundersnot available
KeywordsIsometric exerciseMotor unitVastus lateralis muscleResistance trainingPhysical medicine and rehabilitationElectromyographyMedicineMotor unit recruitmentContraction (grammar)CardiologyAnatomyInternal medicineMathematicsSkeletal muscle

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to test the hypothesis that average motor unit firing rates change in parallel with the contractile properties of vastus lateralis following 8 wk of isometric resistance training. METHODS: The firing rates from more than 400 motor units of vastus lateralis were obtained during voluntary isometric contractions of 50% MVC, before and again after training in male subjects (N = 10) and their untrained controls (N = 10). Single motor unit spike trains were recorded with tungsten microelectrodes. RESULTS: Training resulted in a 36% (P < 0.05) increase in MVC. We also found significant increases (P < 0.05) in maximal twitch amplitude (+17%), time to peak tension (+9%) and the maximal instantaneous rate of contraction (+20%) in the trained leg of the experimental group. Neither the maximal integrated EMG nor the rate of increase of integrated EMG was different after training. There were no significant changes in any of these measures from the untrained leg or the control group. Average firing rates were not different after training despite the increase in twitch contractile speed. CONCLUSION: These findings suggest that the control properties of the nervous system are not altered despite sizable changes in the contractile properties of muscle following 8 wk of resistance training.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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 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

Citations80
Published2000
Admission routes1
Has abstractyes

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