Lower Extremity Kinematics of the Y-Balance Test in Healthy and ACL Injured Adolescent Females
Bibliographic record
Abstract
BACKGROUND: Adolescent females are at significant risk for sustaining an ACL injury. The Y-Balance Test (YBT) is frequently used to evaluate neuromuscular control and lower extremity function. However, few studies have quantified 2D lower extremity kinematics during performance of the YBT, and there is an absence of kinematic data specific to at-risk adolescent females. PURPOSE: To examine lower extremity joint kinematics during execution of the YBT by healthy and ACL-injured adolescent females. STUDY DESIGN: Prospective cohort. METHODS: Twenty-five healthy and ten ACL-injured (mean time from injury 143 days) adolescent females were assessed using the YBT. Sagittal and frontal plane knee and ankle motion was video recorded during execution of the YBT anterior reach movement. Ankle dorsi-flexion, knee flexion, and knee valgus angles were quantified via kinematic analysis. ANOVAs with a post hoc Bonferroni correction were used to compare YBT scoring (%LL) and kinematic data between groups. Pearson product-moment correlations determined the relationship between kinematic data and YBT scoring. RESULTS: Healthy and ACL-injured subjects demonstrated similar YBT scores and lower extremity kinematic data. Healthy subjects demonstrated a weak positive correlation between ankle dorsiflexion and YBT scoring, and a weak negative correlation between knee valgus and YBT scoring. These relationships did not exist for ACL-injured subjects. Kinematic data for both groups also demonstrated a large degree of variability, regardless of YBT score. CONCLUSIONS: Adolescent females frequently utilize a variety of lower extremity movement strategies when performing a functional movement task, and scoring on the YBT offers limited insight regarding lower extremity joint kinematics and ACL-injury risk in a physically active adolescent female population. LEVEL OF EVIDENCE: Level 3.
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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) | 0.002 | 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".