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Record W2806004721 · doi:10.1152/jn.00848.2017

Cutaneous afferent innervation of the human foot sole: what can we learn from single-unit recordings?

2018· review· en· W2806004721 on OpenAlexafffund
Nicholas D. J. Strzalkowski, Ryan M. Peters, J. Timothy Inglis, Leah R. Bent

Bibliographic record

VenueJournal of Neurophysiology · 2018
Typereview
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of British ColumbiaUniversity of GuelphUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMicroneurographyProprioceptionSensory systemMechanoreceptorReceptive fieldSomatosensory systemAfferentNeuroscienceBalance (ability)PsychologyFoot (prosody)Physical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Cutaneous afferents convey exteroceptive information about the interaction of the body with the environment and proprioceptive information about body position and orientation. Four classes of low-threshold mechanoreceptor afferents innervate the foot sole and transmit feedback that facilitates the conscious and reflexive control of standing balance. Experimental manipulation of cutaneous feedback has been shown to alter the control of gait and standing balance. This has led to a growing interest in the design of intervention strategies that enhance cutaneous feedback and improve postural control. The advent of single-unit microneurography has allowed the firing and receptive field characteristics of foot sole cutaneous afferents to be investigated. In this review, we consolidate the available cutaneous afferent microneurographic recordings from the foot sole and provide an analysis of the firing threshold, and receptive field distribution and density of these cutaneous afferents. This work enhances the understanding of the foot sole as a sensory structure and provides a foundation for the continued development of sensory augmentation insoles and other tactile enhancement interventions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.155
GPT teacher head0.342
Teacher spread0.187 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations144
Published2018
Admission routes2
Has abstractyes

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