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Record W4414371716 · doi:10.14814/phy2.70520

Spinal excitability following sensory electrical stimulation of the upper limb

2025· article· en· W4414371716 on OpenAlexafffund
Devin Box, Joshua W. Cohen, Tanya D. Ivanova, Mary E. Jenkins, Anita Christie, S. Jayne Garland

Bibliographic record

VenuePhysiological Reports · 2025
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsH-reflexStimulationUpper limbStimulus (psychology)Sensory systemReflexConditioningTranscranial magnetic stimulation

Abstract

fetched live from OpenAlex

The purpose of the study was to determine if sensory electrical stimulation (SES), below motor threshold, would reduce spinal excitability via reciprocal inhibition (RI) and determine if any changes were sex-related. Eighteen healthy participants (11 males and 7 females) participated in a pre-post comparison study. The Hoffmann (H-) reflex was elicited to assess the spinal excitability of Flexor Carpi Radialis (FCR) and the influence of RI from Extensor Carpi Radialis Longus (ECRL) on FCR using a paired conditioning pulse paradigm. A 15-min bout of SES (4 pulse bursts at 100 Hz) was applied to ECRL, and the H-reflexes were measured at 0- and 20-min post SES. A linear mixed model regression analysis was performed to evaluate the effects of stimulus order, conditioning, sex, and time on the FCR H-reflex. All participants experienced RI from the conditioning pulses, with females having significantly greater suppression than males (mean difference; MD = 0.026). For males, SES produced a depression in FCR excitability (MD = 0.023 at time 0; MD = 0.015 at 20 min post-SES) with no changes in RI. SES had no effect on FCR excitability or RI in females. The potential for SES to produce changes in antagonist excitability was sex-related, which may have important rehabilitation considerations.

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)0.004
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 score0.954
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 head0.047
GPT teacher head0.309
Teacher spread0.262 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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