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Record W4242687573 · doi:10.1109/iembs.2006.4398055

On the Activation Threshold of Nerve Fibers Using Sinusoidal Electrical Stimulation

2006· article· en· W4242687573 on OpenAlexaff
Swarna Sundar, José A. González-Cueto

Bibliographic record

VenueConference proceedings · 2006
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSine waveSubthreshold conductionFiberMaterials scienceStimulationAmplitudeBiomedical engineeringPhysicsNeuroscienceMedicineComposite materialOpticsVoltagePsychology

Abstract

fetched live from OpenAlex

Carpal Tunnel Syndrome (CTS) diagnosis could be improved by selectively activating different types of nerve fibers traversing the carpal tunnel based on their diameter. The objective of this study was to establish the types of fibers activated by different sinusoidal electrical stimuli. The frequencies selected correspond to those used in an available application known as current perception threshold (CPT). This method has been proposed in the literature to assess the severity of CTS in a patient. CPT operates by varying the amplitude and frequency of stimulating sine wave currents. Subthreshold and suprathreshold responses of nerve fibers were modeled in this study using McNeal's model and Frankanhaeuser-Huxley equations. Simulations were performed in MATLAB to determine the stimulating thresholds for different diameter groups of nerve fibers. The study concluded that large A-ß fibers can be activated alone at the 2000Hz frequency, the intermediate A-δ fibers can be activated at the 250Hz frequency in company of A-ß fiber activity, and for fibers with diameter less than 2.5μm to be activated at the 5Hz frequency there must be accompanying activity from A-ß and A-δ fibers.

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 score0.524
Threshold uncertainty score0.306

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 head0.057
GPT teacher head0.263
Teacher spread0.205 · 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
Published2006
Admission routes1
Has abstractyes

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