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Record W2097050323 · doi:10.1002/mus.21184

Responses of human motoneurons to high‐frequency stimulation of Ia afferents

2008· article· en· W2097050323 on OpenAlexafffund
Parveen Bawa, Gordon Chalmers

Bibliographic record

VenueMuscle & Nerve · 2008
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMotor unitStimulus (psychology)ElectromyographySoleus muscleStimulationNeuroscienceCATSChemistryMotor unit recruitmentMedicineAnatomySkeletal muscleInternal medicineBiologyPsychology

Abstract

fetched live from OpenAlex

This study was designed to extend to humans the findings of classical studies on anesthetized cats, which have examined the discharge of spinal motoneurons in response to high-frequency stimulus trains delivered to Ia afferents. Experiments were conducted on the monosynaptic pathway in the flexor carpi radialis (FCR) and soleus muscles. Subjects maintained a rhythmic discharge of a single motor unit (SMU) in either the FCR or soleus while homonymous Ia afferents were stimulated with either a single- or multipulse train. An n@IPI stimulus train had n pulses (n = 2-4) and an interpulse interval (IPI) of 1-8 ms. For each condition and motor unit, surface electromyographic (EMG) activity was averaged, and peristimulus-time histograms (PSTHs) were constructed for the SMU. The magnitude of the EMG was high for IPI = 1 ms, low for IPI = 2-3 ms, and high for IPI = 4-8 ms. SMU responses showed a similar pattern, which indicated that the increased EMG response was due to the presence of multiple peaks in a PSTH. The key results indicate that: (1) a short, high-frequency stimulus train enhances the discharge probability of a motoneuron above that observed with a single pulse; and (2) the increased motoneuron responses are significantly greater for the FCR than for the soleus muscle.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.261
Teacher spread0.233 · 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 designBench or experimental
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

Citations9
Published2008
Admission routes2
Has abstractyes

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