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Record W1774269916 · doi:10.3920/cep150009

A validated approach for collecting fine-wire electromyographic recordings in four canine shoulder muscles during highly dynamic tasks

2015· article· en· W1774269916 on OpenAlexafffund
Kimberley Cullen, James P. Dickey, Stephen H.M. Brown, Stephanie Nykamp, Leah R. Bent, J. J. Thomason, N M M Moens

Bibliographic record

VenueComparative Exercise Physiology · 2015
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of GuelphWestern UniversityInstitute for Work & Health
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBicepsCadaveric spasmElectromyographyProtocol (science)Physical medicine and rehabilitationBiomedical engineeringMedicineComputer scienceAnatomy

Abstract

fetched live from OpenAlex

This study investigated the feasibility of obtaining ultrasound-guided intramuscular fine-wire electromyographic (fEMG) recordings from four canine shoulder muscles during highly dynamic activities. Four cadaveric canines were utilised to confirm the appropriate anatomical landmarks and the use of real time ultrasound guidance for electrode placement for four shoulder muscles: Biceps Brachii (BB), Supraspinatus (SP), Infraspinatus (IF), and Triceps Brachii – Long Head (TBLH). Electromyographic activity of the left BB, S P, IF, and TBLH was then recorded in two research dogs while walking and trotting to refine the data collection procedures. Finally, the full experimental protocol was piloted with two client-owned, specially-trained agility dogs, confirming the feasibility of collecting fEMG recordings while performing dynamic, highly-specific agility-related tasks and verifying our EMG amplitude normalisation protocol to enable comparisons across muscles and performance tasks. We present specific guidelines regarding the placement of fEMG electrodes and data collection/normalisation procedures to enable investigations of muscle activation during dynamic activities.

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 categoriesMeta-epidemiology (narrow)
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 score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.049
GPT teacher head0.280
Teacher spread0.231 · 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.

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

Citations2
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueComparative Exercise PhysiologySame topicMuscle activation and electromyography studiesFrench-language works237,207