DIFFERENCES IN THE CONTRIBUTION OF EACH LOWER LIMB IN THE DROP VERTICAL JUMP TASK SHOULD BE ASSESSED IN ANTERIOR CRUCIATE LIGAMENT-INJURED PAEDIATRIC POPULATION
Bibliographic record
Abstract
To investigate differences in the drop vertical jump height in female adolescents with an ACL injury and healthy controls and the contribution of each limb in this task. Forty female adolescents with an ACL injury (ACLi, 15.2 ± 1.4 yrs, 164.6 ± 6.0 cm, 63.1 ± 10.0 kg) and thirty-nine uninjured (CON, 13.2 ± 1.7 yrs, 161.7 ± 8.0 cm, 50.6 ± 11.0 kg) were included in this study. A 10-camera infrared motion analysis system (Vicon, Nexus, Oxford, UK) tracked pelvis, thigh, shank, and foot kinematics at 200Hz, while the participants performed 3 trials of double-legged drop vertical jumps (DVJ) on two force plates (Bertec Corp., Columbus, USA) sampled at 2000Hz.The maximum jump height normalised by dominant leg length was compared between groups using independent samples t-test. The maximum vertical ground reaction force (GRFz) and sagittal ankle, knee and hip velocities before take-off were compared between limbs in both groups, using paired samples t-test. The normalised jump height was 11% lower in the ACLi than in the CON (MD=0.04 cm, p=0.020). In the ACLi, the maximum GRFz (MD=46.17N) and the maximum velocities of ankle plantar flexion (MD=79.83°/s), knee extension (MD=85.80°/s), and hip extension (MD=36.08°/s) were greater in the non-injured limb, compared to the injured limb. No differences between limbs were found in the CON. ACL injured female adolescents jump lower than the healthy controls and have greater contribution of their non-injured limb, compared to their injured limb, in the DVJ task. Clinicians should investigate differences in the contribution between limbs during double-legged drop vertical jump when assessing patients with an ACL injury, as this could help identify asymmetries, and potentially improve treatment, criteria used to clear athletes to sport, and re-injury prevention.
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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 | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".