Association Between the Functional Movement Screen and Landing Kinematics in Individuals With and Without Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to compare functional movement screen (FMS) scores and drop vertical jump (DVJ) kinematics between those with and without anterior cruciate ligament reconstruction (ACLR), and to evaluate the association between FMS composite score and DVJ kinematics. DESIGN: Cross-sectional. PARTICIPANTS: Sixty individuals with and without a history of ACLR. MAIN OUTCOME MEASURES: Composite FMS score and the dorsiflexion, knee-flexion, hip-flexion, knee abduction, hip adduction, and trunk-flexion angles during a DVJ. RESULTS: The FMS scores did not differ between groups (P > .05). There were smaller peak and initial contact hip-flexion angles in the ACLR and contralateral limbs compared with controls, and smaller peak dorsiflexion angles in the ACLR compared with contralateral limbs (P < .05). Lower FMS score was associated with a smaller peak dorsiflexion angle, smaller peak knee-flexion angle, and larger peak knee abduction angle in the ACLR limb (ΔR2 = .14-.23); a smaller peak dorsiflexion angle and smaller peak knee-flexion angle in the contralateral limb (ΔR2 = .17-.19); and a smaller peak dorsiflexion angle, smaller peak knee-flexion angle, and larger peak knee abduction angle in the control limb (ΔR2 = .16-.22). CONCLUSION: The FMS scores did not differ between groups, but were associated with DVJ kinematics and should be a complementary rather than substitute assessment.
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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 | 0.000 |
| 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".