Drop Vertical Jump Biomechanics Differ In Athletes Post-concussion: Implications For Concussion Detection And Rehabilitation
Bibliographic record
Abstract
Athletes recovering from concussions face cognitive impairments predisposing them to higher risk for lower extremity injuries including anterior cruciate ligament (ACL) tears. Current concussion detection methods are limited by observer interpretation and intentional underperforming on baseline testing (sandbagging). Kinematics can aid in objective concussion detection; however, studies investigating changes in jump landing kinematics from pre- to post-concussion are limited. PURPOSE: To investigate changes in lower extremity and trunk kinematics during a drop vertical jump (DVJ) in collegiate varsity athletes from pre- to post-concussion. METHODS: 20 collegiate varsity athletes performed 3 DVJs at the preseason assessments directly before and after sustaining a concussion, captured by a Kinect V2 device. Specific DVJ parameters (Table 1), and the standard deviation of each parameter across a participant’s 3 jump (inter-jump variability, IJV), were compared between pre- and post-concussion using a paired one-sided t-test. Statistical significance was set at P < .05. RESULTS: Peak sagittal angle of left and right knees decreased significantly post-concussion, indicating less knee flexion (105.66° vs 91.74°, P = .011; 105.01° vs 91.30°, P = .013). Maximum spine sway in the X axis decreased significantly from 4.37° to 3.41° (P = .048). IJV of peak sagittal angle increased significantly in both knees (5.81° vs 15.71°, P < .01; 4.63° vs 15.29°, P < .01). IJV of spine sway variability in the Z axis and maximum ankle distance increased significantly (0.42° vs 0.72°, P = .024; 0.017 m vs 0.029 m, P = .022). CONCLUSIONS: Multiple DVJ parameters change significantly in collegiate varsity athletes post-concussion. Notably, decreased peak sagittal angle has been associated with increased risk for ACL tear. The DVJ is a potentially valuable tool for objective concussion detection and guiding post-concussion rehabilitation to reduce the risk of ACL tears. Supported by: This work was supported by MEDTEQ+, Emovi Inc., and Semperform inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".