Assessing and modifying neuromuscular risk factors for anterior cruciate ligament injury in female athletes
Bibliographic record
Abstract
Injury to the anterior cruciate ligament (ACL) may have consequences for an athlete in the form of pain, decreased activity levels and early-onset osteoarthritis. Female athletes are at increased risk of injury, perhaps because of differences in neuromuscular function. Methods of identifying risk factors and effective prevention strategies for ACL injury have traditionally involved athletes of high school age or older. However by that age, the opportune time to intervene may have passed. This thesis involves a sequence of studies which measures the neuromuscular function of younger athletes, aged 10-14 years. First, the reliability of measuring strength and power of the lower extremity on an isokinetic dynamometer was explored. Torque and power of the hip flexors and knee extensors were the only measures with acceptable reliability. Conversely, peak velocity of all the tested hip and knee movements demonstrated acceptable reliability. A high amount of variability was found with all test movements, and so alternate tests should be used if measuring an individual athlete’s ability. From the data collected within the reliability study, a sex comparison was undertaken to determine if neuromuscular power differed at this young age. It was determined that girls and boys between 10 and 14 years of age do not differ in terms of knee or hip movement velocity or power. There is evidence to suggest that sex differences exist by adulthood; further research is required to determine when the disparity becomes apparent. The final project was to determine whether strength training would improve the manner in which young female athletes land from a jump; a common ACL injury mechanism. This randomized controlled trial found no difference between the intervention group who trained their legs, and the control group who trained their arms. However those athletes with the poorest landings appeared to improve their movement pattern regardless of training regime. This thesis contributes to the literature by providing evidence for measurement protocols for young athletes, introducing neuromuscular power instead of strength into the investigation of contributing factors to injury, and by furthering the examination of strength training as an effective component of prevention 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".