The Application of Acoustic Myography in Canine Muscle Function and Performance Testing
Bibliographic record
Abstract
It is generally known that muscles have the ability to store and convert energy making certain types of movement highly efficient with regard to muscle work. The principle behind this concept is summarized in what has become known as the “spring-mass” model. Whilst evidence of a change in gait from walk to trot has been documented for more than 35 years, quantitative measurements of comparable changes in muscle function remain elusive. In a study involving 11 Labrador dogs, looking at muscle function as assessed by acoustic myography, changes in muscle efficiency/coordination as well as both spatial-(fiber recruitment) and temporal-(firing rate) summation were made for both walk and trot for m.longissimus lumborum and m.gluteus superficialis. In m.gluteus superficialis a 35% decrease (P < 0.05) in efficiency was noted when dogs changed gait from walk to trot, and both muscles showed a slight yet significant increase in fiber recruitment. However, both muscles showed a significant decrease in firing rate (36% and 76% for m.gluteus superficialis and m.longissimus lumborum; P < 0.05 & P < 0.01, respectively) when changing pace from walk to trot. It is concluded that acoustic myography not only enables quantifiable assessment of gait changes with regard to the “spring-mass” model, but that the observed changes in the two muscles measured confirm, as well as advance our understanding of the mechanisms by which reductions in muscle work during steady level movements are achieved. You can submit your Manuscripts at: https://symbiosisonlinepublishing.com/submitManuscript.php
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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".