Computational methodology to study the effect of cable-stabilized knee brace on anterior cruciate ligament strain during single-leg jump landing
Bibliographic record
Abstract
Knee bracing is commonly used for rehabilitation after ligament surgery. However, the effectiveness of knee bracing in preventing ligament injuries is not widely studied. This study aimed to develop a computational methodology to investigate the effectiveness of a novel type of cable-stabilized knee brace on anterior cruciate ligament (ACL) strain during single-leg jump landing. The brace features a compliant design with nonextensible pretensioned cables integrated within a compression tight garment. A combined in vivo/in silico method was developed for this purpose. A computational model of the cable-stabilized knee brace was developed with linked truss elements used to simulate the cable. The cables were integrated into an existing computational model of the knee. Subsequently, single-leg jump landing simulations were conducted on the model, using muscle forces and joint kinematic/kinetic profiles from 10 participants. Anterior cruciate ligament strain behaviors were then compared between the braced and unbraced configurations. The computational methodology was successful in simulating the differences in ACL strain because of the brace. The average peak ACL strain in the braced configuration was 4.99% ± 2.36% and in the unbraced configuration was 3.23% ± 2.31% ( p = 0.091). The methodology developed lays the groundwork for future advancements in optimizing the cable-stabilized knee brace design and refining its potential in preventing ligament injuries.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".