ANTERIOR CRUCIATE LIGAMENT STRAIN DURING STOP-JUMP LANDING: A COMPUTATIONAL STUDY
Bibliographic record
Abstract
Stop-jump landing maneuvers are a particularly common source of injury to the anterior cruciate ligament (ACL). The objective of this study is to determine which kinematic and kinetic parameters contributed to greater ACL strains during stop-jump landings. A combined in vivo/computational modeling approach was used to simulate stop-jump landing activity. Motion capture data from five human participants performing a non-injurious stop-jump landing were used to compute subject- specific muscle forces and 3D kinematics of the knee. These outputs were subsequently used to simulate the activity on a validated computational model of the knee. Correlation analysis was conducted to determine which variables significantly affected the strain in the ACL. The resulting average peak ACL strain during the activity was 7.9 ± 2.4%. A bivariate correlation study found that there was a strong correlation between ACL strain and the knee range of flexion during the period from ground contact to peak ground reaction force (GRF) time ([Formula: see text] = −0.81, [Formula: see text] = 0.08). Neither the quadriceps force nor the instantaneous knee flexion angle during landing or peak GRF was strongly correlated to ACL strain. The results show that strain in the ACL during stop-jump landing could be reduced by increasing the knee range of flexion over time during the landing phase.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".