Finite element model of intramuscular pressure during isometric contraction of skeletal muscle
Bibliographic record
Abstract
The measurement of in vivo intramuscular pressure (IMP) has recently become practical and IMP appears well correlated with muscle tension. A numerical model of skeletal muscle was developed to examine the mechanisms producing IMP. Unipennate muscle is modelled as a two-dimensional material continuum that is incompressible and nonlinearly anisotropic. The finite element technique is used to calculate IMP and muscle stress during passive stretch and during isometric contraction. A novel element models the contractile portion of muscle, incorporating sarcomere length-force and velocity-force relations. A range of unipennate muscle geometries can be modelled. The model was configured to simulate the rabbit tibialis anterior muscle over a range of lengths. Simulated IMP and stress results were validated against animal experimentation data. The simulation agreed well with the experimental data over the range of 0.8-1.1 of the optimal length. Severe pressure gradients were produced near the musculo-tendinous junctions while IMP was more uniform in the central muscle belly. IMP and muscle stress in relaxed (unstimulated) muscle increased nonlinearly with muscle length. IMP and stress in isometrically contracting muscle showed a local maximum at optimal length and were reduced at shorter lengths. At muscle lengths longer than optimal, stress and IMP increased predominately due to tension in the passive elastic structures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".