Intramuscular fibre conduction velocity and muscle fascicle length in human vastus lateralis
Bibliographic record
Abstract
Muscle fascicle length and muscle fibre conduction velocity (MFCV) are thought to be important parameters for power performance. It might be expected that faster muscle fibre conduction velocities would compensate for longer fascicle lengths to increase the speed of action potential propagation along the elongated fibres. However, the relationship between muscle fascicle length and MFCV remains unknown. The aim of the present study was to explore the relationship between average vastus lateralis MFCV and average fascicle length. In 17 moderately trained, healthy, male, physical education students (age, 23.4 ± 3.1 years; body height, 178 ± 5.5 cm; body mass, 82.7 ± 6.9 kg; body mass index, 24.6 ± 1.5 kg·m−2) resting MFCV was measured with intramuscular microelectrodes while muscle architecture was evaluated with ultrasonography. Fascicle length was highly correlated with total MFCV (r = 0.923, p = 0.000), maximum MFCV (r = 0.949, p = 0.000), and MFCV of the fastest (r = 0.709, p = 0.001), but not of the slowest fibres (r = 0.131, p = 0.616). No significant correlations were also found between vastus lateralis thickness or fascicle angle with any of MFCV parameters (r = 0.145–0.430; R2 < 0.130; p > 0.05). These data indicate that average MFCV is associated with average fascicle length in vastus lateralis muscle in different individuals. It seems that participants with longer fascicle lengths have also higher MFCVs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".