Combined in Vivo/in Vitro Method to Study Anteriomedial Bundle Strain in the Anterior Cruciate Ligament Using a Dynamic Knee Simulator
Bibliographic record
Abstract
The mechanism of noncontact anterior cruciate ligament (ACL) injury is not well understood. It is partly because previous studies have been unable to relate dynamic knee muscle forces during sports activities such as landing from a jump to the strain in the ACL. We present a combined in vivo/in vitro method to relate the muscle group forces to ACL strain during jump-landing using a newly developed dynamic knee simulator. A dynamic knee simulator system was designed and developed to study the sagittal plane biomechanics of the knee. The simulator is computer controlled and uses six powerful electromechanical actuators to move a cadaver knee in the sagittal plane and to apply dynamic muscle forces at the insertion sites of the quadriceps, hamstring, and gastrocnemius muscle groups and the net moment at the hip joint. In order to demonstrate the capability of the simulator to simulate dynamic sports activities on cadaver knees, motion capture of a live subject landing from a jump on a force plate was performed. The kinematics and ground reaction force data obtained from the motion capture were input into a computer based musculoskeletal lower extremity model. From the model, the force-time profile of each muscle group across the knee during the movement was extracted, along with the motion profiles of the hip and ankle joints. This data was then programmed into the dynamic knee simulator system. Jump-landing was simulated on a cadaver knee successfully. Resulting strain in the ACL was measured using a differential variable reluctance transducer (DVRT). Our results show that the simulator has the capability to accurately simulate the dynamic sagittal plane motion and the dynamic muscle forces during jump-landing. The simulator has high repeatability. The ACL strain values agreed with the values reported in the literature. This combined in vivo/in vitro approach using this dynamic knee simulator system can be effectively used to study the relationship between sagittal plane muscle forces and ACL strain during dynamic activities.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".