Biomechanical Risk Factors for Increased Anterior Cruciate Ligament Loading and Injury: A Systematic Review
Bibliographic record
Abstract
Background: Understanding the biomechanical risk factors for noncontact anterior cruciate ligament (ACL) injury can inform machine learning models, aid in prevention strategies, and guide rehabilitation protocols, reducing the incidence and burden of these injuries in both athletes and the general population. Purpose: To determine the biomechanical risk factors associated with noncontact ACL injury and increased knee loading. Study Design: Systematic review; Level of evidence, 4. Methods: A literature search was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Randomized, cohort, case-control, and cross-sectional studies identifying noncontact biomechanical risk factors for ACL injuries published before May 2023 were included in this review. Excluded were studies focused on contact ACL injuries, those focused on biomechanical risk factors postinjury, and those not published in the English language. The authors highlighted biomechanical risk factors not extensively covered in previous reviews, including the toe-in position, increased contralateral pelvic hike, increased hip internal rotation angle, and specific ankle angles. A quantitative overview of the included studies was conducted, highlighting the frequency of each biomechanical factor reported as potentially related to ACL injury or loading risk. Results: A total of 28 studies (2819 athletes) were selected for analysis. The majority of these studies (22/28) were cross-sectional, primarily assessing ACL load indirectly via knee valgus moment or ground-reaction forces, while case-control and cohort studies focused on ACL injury incidence. Overall, 83% (5/6) of the studies assessing upper body biomechanics found that trunk flexion/extension and perturbations affect ACL loading risk. Of studies assessing hip biomechanics, 83% (10/12) showed increased ACL loading or injury risk with increased hip abduction/internal rotation angles. For the foot and ankle, increased toe-in/toe-out landing in 67% of studies (2/3) demonstrated higher stress on the ACL. Knee biomechanics were associated with increased ACL loading in 100% of the respective studies (5/5), with decreased knee flexion angles leading to increased loading. Conclusion: The data demonstrated that factors associated with increased medial knee alignment, sagittal alignment of the trunk, and decreased lateral trunk flexion reduced both knee loading and ACL injury risk. Targeted prevention and detection strategies addressing high-risk biomechanics may reduce injury incidence, underscoring the need for further research to optimize intervention programs.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".