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The Utility of Interappendicular Connections in Bipedal Locomotion

2017· review· en· W2575607009 on OpenAlexaff
David W. McMillan, Ray D. de Leon, Pierre A. Guertin, Christine J. Dy

Bibliographic record

VenueCurrent Pharmaceutical Design · 2017
Typereview
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBipedalismNeurosciencePhysical medicine and rehabilitationSpinal cord injuryBiologySpinal cordAnatomyMedicine

Abstract

fetched live from OpenAlex

Homo sapiens constitute the only currently obligate bipedal mammals and, as it stands, upright bipedal locomotion is a defining characteristic of humans. Indeed, while the evolution to bipedalism has allowed for the upper limbs to be liberated from ground contact during ambulation, their role in locomotion is far from obsolete. Rather, there is reason to believe that arm swing offers important mechanical and neurological advantages to bipedal locomotion. In this short review, we present some compelling findings on the neural connections between the arms and legs during human locomotion. We begin with a description of the importance of arm swing during walking from a mechanical perspective. Then, we examine evidence for the existence of interappendicular connections that converge along with peripheral afferents, descending inputs, and propriospinal projections, onto the neural circuits innervating the muscles of the arms and legs. The varied effects of interappendicular coupling on the neural control of locomotion are also examined in cases of neurological injury. We use the insight gained from these collected works as well as those from our own studies on locomotor training to discuss strategies to use interappendicular connections to rehabilitate walking in individuals experiencing loss of function after debilitating spinal cord injury.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.325
GPT teacher head0.449
Teacher spread0.124 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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