Tired of ACL Injures: A Review of Methods and Outcomes of Neuromuscular Fatigue as a Risk Factor for ACL Injuries
Why this work is in the frame
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Bibliographic record
Abstract
Background/Objectives: One potential risk factor that remains especially contentious in the anterior cruciate ligament (ACL) injury literature is the role of neuromuscular fatigue in ACL injury risk. Therefore, the purposes of this review are (i) to present the research and practical concepts of lower extremity neuromuscular fatigue; and (ii) to review the literature relating to neuromuscular fatigue as an ACL injury risk factor and mechanism. Methods: A structured review was performed in the Medline database using a search strategy that included terms such as “anterior cruciate ligament injury” and “knee injuries” combined with terms such as “injury” and “fatigue”. Articles were included if they included young healthy participants (18–35) and made a comparison between non-fatigued and fatigued states that were assessed with at least one lower extremity biomechanical variable associated with ACL injury risk. Results: Overall, there were 67 studies included, accounting for 1440 participants (627 male and 813 female) across a variety of sports and activities. Of these, 53 (79%) reported a post-fatigue change in the kinematics, kinetics, neuromuscular, and/or other (e.g., proprioceptive) outcomes that indicate that the participants would be at an increased risk of an ACL injury. The most common argument against fatigue as a risk factor is that ACL injuries do not tend to occur later in a game or season, when it is assumed that athletes would be most fatigued. Conclusions: The evidence presented in this review suggests that localized neuromuscular fatigue is a risk factor, among multiple factors, for ACL injuries, providing another modifiable risk factor that should be considered when developing ACL injury risk reduction interventions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it