Is there evidence that proprioception or balance training can prevent anterior cruciate ligament (ACL) injuries in athletes without previous ACL injury?
Bibliographic record
Abstract
A collegiate-level soccer player was instructed by her coach to incorporate a proprioceptive component into her training program. He suggested that she purchase a balance board and immediately begin a program that he designed. She approached her physical therapist (SJF) for more information. I immediately recognized that, because of her sex and sport of choice, she would be at high risk for an anterior cruciate ligament (ACL) injury. Hewett et al1 estimated that as many as 2,200 ACL ruptures per year occur in female collegiate athletes in both the recreational and competitive ranks. Treatment and rehabilitation costs are estimated at $17,000 per ACL injury, which do not take into account the potential loss of long-term participation, loss of scholarship funding, and future disability from arthritic changes in a reconstructed knee.1 For these reasons, a shift toward injury prevention is warranted. Injury prevention for the ACL can take many forms, including a variety of training protocols, athlete education, and bracing. Current studies focus on neuromuscular training as a preventive measure, with programs that include strength, flexibility, plyometrics, sport-specific agility drills, speed enhancement, balance, and athlete education.1–7 A clinician who understands the individual components of these programs could optimize injury prevention and aid athletes in appropriate program design and equipment purchases. In the case of this athlete, my colleagues and I focused on the use of proprioception or balance training and its effect on incidence of ACL injury.
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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 | 0.028 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".