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Record W2057086426 · doi:10.1113/jphysiol.2002.025064

Effect of caffeine on self‐sustained firing in human motor units

2002· article· en· W2057086426 on OpenAlexaff
Christine Walton, Jayne M. Kalmar, E. Cafarelli

Bibliographic record

VenueThe Journal of Physiology · 2002
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsYork University
Fundersnot available
KeywordsCaffeineExcitatory postsynaptic potentialPlaceboMonoamine neurotransmitterAnesthesiaFacilitationInhibitory postsynaptic potentialPharmacologyMedicineChemistrySerotoninEndocrinologyPsychologyInternal medicineNeuroscience

Abstract

fetched live from OpenAlex

This study examined the effect of caffeine on self-sustained firing (SSF) of human motor units. At physiological doses, caffeine acts as a competitive antagonist to the inhibitory effects of adenosine. This antagonism has many possible effects on the central nervous system. One of these effects is to increase the release of the excitatory neurotransmitters serotonin and noradrenaline. In addition, caffeine increases serotonin concentration in brainstem regions that have excitatory projections to spinal motor neurons. Since plateau potentials, which are responsible for SSF, are facilitated by these neurotransmitters, we hypothesized that caffeine would increase the frequency at which SSF occurs. A double-blind, repeated-measures design using either drug (6 mg kg(-1) caffeine) or placebo (flour) was carried out on seven male subjects who reported ingesting less than 200 mg week(-1) caffeine. We investigated the occurrence of SSF in tibialis anterior motor units (214 trials) and found a significant (P < 0.05) increase in the occurrence of SSF in the caffeine trial (87.0 +/- 5.8 %) compared to the placebo (64.6 +/- 9.7 %). These data further verify the presence of SSF in the tibialis anterior motor units of young men and provide indirect evidence of the facilitation of plateau potentials by monoamines in the human neuromuscular system.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.212

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 head0.033
GPT teacher head0.288
Teacher spread0.255 · 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

Citations82
Published2002
Admission routes1
Has abstractyes

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