The effect of Stop-X injury prevention program on landing mechanics and core stability in military cadets
Bibliographic record
Abstract
Background. Poor landing mechanics and core stability are risk factors contributing to knee injuries, especially Anterior Cruciate Ligament injury, in military cadets. This study aimed to investigate the effect of the Stop-X injury. Methods. In this quasi-experimental study, 40 cadets were purposefully recruited and randomly assigned either to an intervention (INT, n=20, age=19.05±0.68 years, height=1.75±0.06 m, weight=72.70±4.18 kg, BMI=23.77±1.68 kg/m2) or control group (n=20 participants, age=18.70±0.65 years, height=1.77±0.06 m, weight=74.10±4.90 kg, BMI=23.53±2.24 kg/m2). Landing Error Scoring System and McGill’s stability tests were used to evaluate landing mechanics and core stability at the baseline and the end of the study. Then, the INT group performed the Stop-X program as a warm-up program before each training session for eight weeks, whereas the CON group carried out their routine warm-up program during this time. Mann-Whitney U and ANCOVA tests were used to evaluate the changes. Results. The results obtained in the intervention group in post-test in comparison with the control group showed that there was a significant reduction in Landing Error Scoring System test scores (P=0.001), and there were significant enhancements in core stability tests (P=0.001). Moreover, the results in the intervention group revealed significant reduction in Landing Error Scoring System test scores (P=0.001) and significant enhancements in core stability tests (P=0.001), but there no significant differences were observed in the control group (P>0.05). Conclusion. In sum, the Stop-X injury prevention program may have improved landing mechanics and enhanced core stability in military cadets. Thus, Stop-X program may have reduced the risk factors associated with knee injuries in military cadets. Practical Implications. Our findings suggested that the Stop-X injury prevention program may have been used as a suitable warm-up program in military service instead of traditional warm-up to improve the neuromuscular and biomechanical risk factors.
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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 | 0.000 |
| 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) | 0.003 | 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".