The effects of regional quadriceps architecture on angle-specific rapid force expression
Bibliographic record
Abstract
Evaluating anatomical contributions to performance can increase understanding of muscle mechanics and guide physical preparation. While the impact of anatomy on muscular performance is well studied, the effects of regional quadriceps architecture on rapid torque or force expression are less clear. Regional (proximal, middle, and distal) quadriceps (vastus lateralis, rectus femoris, and vastus intermedius) thickness (MT), pennation angle (PA), and fascicle length (FL) of 24 males (48 limbs) were assessed via ultrasonography. Participants performed maximal isometric knee extensions at 40°, 70°, and 100° of knee flexion to evaluate rate of force development from 0 to 200 ms (RFD0-200). Measurements were repeated on three occasions with the greatest RFD0-200 and mean muscle architecture measures used for analysis. Linear regression models predicting angle-specific RFD0-200 from regional anatomy provided adjusted correlations (√adj R2) with bootstrapped compatibility limits. Mid-rectus femoris MT (√adj R2 = 0.41–0.51) and proximal vastus lateralis FL (√adj R2 = 0.42–0.48) were the best single predictors of RFD0-200, and the only measures to reach precision with 99% compatibility limits. Small simple correlations were found across all regions and joint angles between RFD0-200 and vastus lateralis MT (√adj R2 = 0.28 ± 0.13; mean ± SD), vastus lateralis FL (√adj R2 = 0.33 ± 0.10), rectus femoris MT (√adj R2 = 0.38 ± 0.10), and lateral vastus intermedius MT (√adj R2 = 0.24 ± 0.10). Between-correlation comparisons are reported within the article. Researchers should measure mid-region rectus femoris MT and vastus lateralis FL to efficiently and robustly evaluate potential anatomical contributions to rapid knee extension force changes, with distal and proximal measurements providing little additional value. However, correlations were generally small to moderate, suggesting that neurological factors may be critical in rapid force expression.
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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 | 0.001 | 0.002 |
| 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".