Musculoskeletal modelling sequentially integrated with stress simulation reveals asymmetrical knee loading and ligament stress during long-distance running
Bibliographic record
Abstract
BACKGROUND: Understanding the internal load characteristics of the knee joint is essential for investigating unilateral knee injuries associated with running. This study examined the differences in the location and magnitude of von Mises stress in the internal structures of bilateral knee joints during the stance phase of gait following 10 km running at submaximal speeds. METHODS: A healthy male recreational runner participated in this study. We employed a synergistic approach, integrating subject-specific knee joint angles, reaction forces, and moments derived from musculoskeletal modeling to inform and drive the finite element (FE) modeling of running. This methodology ensured a detailed and accurate representation of knee joint mechanics. The peak stresses of the bilateral knee menisci, tibial cartilage, and five main ligaments were quantified using a FE model during the stance phase. RESULTS: The meniscus, tibial cartilage, anterior (ACL), posterior cruciate ligament (PCL), medial (MCL), lateral collateral ligament (LCL) and experienced larger loads in the non-dominant limb across most phases of stance. Additionally, fatigue elevated the peak loading on the non-dominant limb's ACL, PCL, and LCL during the gait stance phase but diminished the load on these ligaments in the dominant knee joint. For Patellar ligament (PL), the non-dominant side showed maximal stress at initial contact, while the dominant side dominated during the remaining stance phases. CONCLUSIONS: This proof-of-concept substantially enhances our understanding of the impact of running-induced fatigue on bilateral knee loading. It provides a detailed analysis of factors leading to unilateral knee overload during extended running. These insights are essential in formulating targeted strategies to reduce injury risks.
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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 | 0.000 |
| 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".