Coupling Characteristics of Anterior Cruciate Ligament and Gait Analysis on Anterior Instability of Knee
Bibliographic record
Abstract
Knee is the largest and most complicated joint in the human body. Since in humans the knee supports nearly the whole weight of the body, it is the joint most vulnerable to acute injury. Normal knee joint movements are accomplished via an intricate balance between passive ligamentous and active muscular components to maintain knee stability and prevent injury. The anterior cruciate ligament (ACL) is a critical passive component to normal knee function which acts to resist anterior rotatory motion of the tibia relative to the femur. The objective of this study is to investigate the coupling characteristic of the ACL, also try to search the kinematics and kinetic coupling characteristic make the quadriceps asymmetry after the ACL injured. Meanwhile this paper mainly presents some lower extremity data of the patients who had the anterior instability on the knee due to the ACL injury based on a infrared reflective marker system using stereophotogrammetry techniques. The compensatory mechanism and the biological coupling characteristic are analyzed in the paper. The results of this paper provide fundamental information on further study of kinematics and segmental coupling. The results of time-distance parameters indicate that the efficiency of walking is lower to some extent by decreased gait frequency and speed and prolonged gait circle that lead to unsteadiness of knee after injury. The kinematics data results demonstrate that the joint angle of extremity have adaptable changes produced by nervomuscular control system after injury. The kinemics data show that the other joints of affected extremity and joints of unaffected extremity both have adaptable changes, which is compensation profiting knee stability after injury. Keywords: biological coupling; kinematic coupling; compensatory mechanism
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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 | 0.002 | 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) | 0.001 | 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".