Tibial Strains During Prolonged Downhill Running: A Finite Element Analysis
Bibliographic record
Abstract
Bone strains in the lower extremity may be influenced by neuromuscular fatigue. In this study, we examined potential changes in finite element (FE) predicted tibial strains over the course of a fatiguing downhill-running protocol. Twelve physically active males ran for 30 min on an instrumented treadmill at a speed of 2.8 m s-1 and a grade of -11.3 deg. Motion capture and inverse-dynamic-based static optimization were used to estimate lower-extremity joint contact and muscle forces at the beginning, middle, and end stages of the downhill run. Finite element models of the tibia-fibula complex, from database-matched computed tomography images, were then used to estimate resulting 90th percentile strain (peak strain) and strained volume (volume of elements above 3000 με). In the fatigued state, peak ankle joint contact forces decreased an average of 8.1% (p < 0.002) in the axial direction, but increased an average of 7.7% (p < 0.042) in the anterior-posterior direction; consequently, finite element estimations of peak strain and strained volume were unaffected (p > 0.190). Although neuromuscular fatigue may influence ankle joint contact forces, it may not necessarily influence tibial strains due to the complex, and sometimes nonintuitive, relationship between applied load and resulting bone strain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".