The Application of Acoustic Myography in Canine Muscle Function and Performance Testing
Bibliographic record
Abstract
begin to walk at a faster pace, the limbs begin to impact with the ground and in so doing behave elastically [5].Clearly, there is then an inverse relation between limb stiffness and an ability to convert potential energy into kinetic energy thus stiffness decreases as speed increases.In the trot, the body's center of mass behaves differently.Rather than being raised in the first half of the stance phase, it is lowered and the body moves as a wave, often with minimal oscillation in those breeds of dogs that are used to trotting for long periods of time [6].Here potential energy is converted into elastic energy and stored upon impact with the ground by passive stretching of tendons and muscles, to be released later on in the stance phase.This is the spring-mass model described by Blickhan [3].The study undertaken by Gregersen and colleagues not only confirms this spring-mass model, it also reveals that the work performed by every joint of the fore-and hind-limbs is more efficient due to a reduced "cost" of physical activity resulting from the release of elastic energy [7].Indeed, in the trot three quarters of the work performed by dogs is recoverable [7].Muscles used in locomotion are stretched prior to shortening while producing a force.Compared to concentric contractions (shortening), eccentric contractions (lengthening) result in the highest force with the lowest energy costs due to the higher energy absorption [7,2].Subsequently, the stored energy can be converted back to kinetic energy in the rebound [1,2].One of the situations in which this is used to great effect is in the very rapid protraction of a horse's fore-limb during periods of galloping, when elastic energy stored in the biceps muscle is released as a burst of energy that results in a catapult action propelling the fore-limb forward [8].Acoustic Myography (AMG), which directly measures muscle contractions, was recently documented as being a useful diagnostic tool for rapidly determining muscular injury in the veterinary clinic [9,10].Indeed, this technique has been used in the assessment of veterinary clinical diagnosis and monitoring of such dysfunctions or diseases as lameness, kissing spine, crookedness and muscular atrophy as well as post surgery/ trauma monitoring (distance measurement) or retraining/ healthy daily training (http://www.myodynamikequine.com/
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".