Musculoskeletal Simulation Tools for Understanding Mechanisms of Lower-Limb Sports Injuries
Bibliographic record
Abstract
Musculoskeletal simulation and dynamic modeling programs have been used to gain insight into lower-limb musculoskeletal injury mechanisms. In addition to the temporospatial, kinematic, and kinetic data obtained from motion analysis systems, musculoskeletal simulation programs also can provide information on joint contact and muscle forces, musculotendinous lengths and velocities, and muscle activation levels. Musculoskeletal simulation platforms may help in the assessment of risk factors for sports-related injuries. Using musculoskeletal simulations for injury prevention programs may help lower the incidence of sports injuries, and may allow for fast recovery from injury. In this review, injury mechanisms and risk factors of some of the most common lower-limb musculoskeletal injuries, including anterior cruciate ligament, patellofemoral, and hamstring injuries were summarized from a biomechanical perspective. Also, the efficacy of musculoskeletal modeling and dynamic simulation tools in helping our understanding of these injury mechanisms was discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| 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 | 0.001 | 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".