Computational study of extrinsic factors affecting ACL strain during single-leg jump landing
Bibliographic record
Abstract
Abstract Background Non-contact anterior cruciate ligament (ACL) injuries are a major concern in sport-related activities due to dynamic knee movements. There is a paucity of finite element (FE) studies that have accurately replicated the knee geometry, kinematics, and muscle forces during dynamic activities. The objective of this study was to develop and validate a knee FE model and use it to quantify the relationships between sagittal plane knee kinematics, kinetics and the resulting ACL strain. Methods 3D images of a cadaver knee specimen were segmented (bones, cartilage, and meniscus) and meshed to develop the FE model. Knee ligament insertion sites were defined in the FE model via experimental digitization of the specimen’s ligaments. The response of the model was validated against multiple physiological knee movements using published experimental data. Single-leg jump landing motions were then simulated on the validated model with muscle forces and kinematic inputs derived from motion capture and rigid body modelling of ten participants. Results The maximum ACL strain measured with the model during jump landing was 3.5 ± 2.2%, comparable to published experimental results. Bivariate analysis showed no significant correlation between body weight, ground reaction force and sagittal plane parameters (such as joint flexion angles, joint moments, muscle forces, and joint velocity) and ACL strain. Multivariate regression analysis showed increasing trunk, hip and ankle flexion angles decreases ACL strain (R2 = 90.04%, p < 0.05). Conclusions Soft landing decreases ACL strain and the relationship could be presented through an empirical equation. The model and the empirical relation developed in this study could be used to better predict ACL injury risk and prevention strategies during dynamic activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".