Hamstring to Quadriceps Strength ratio and Non-contact Leg Injuries: A Prospective Study During one Season
Bibliographic record
Abstract
Previous studies have proposed that thigh muscle imbalance is a critical risk factor for the athletic non-contact knee injuries. However, there is a little consensus among prospective studies with regard to the correlation between isokinetic hamstring to quadriceps (H:Q) strength ratio and the non-contact knee injury rates. Most of athletic movements at risk are closed kinetic chain movements, and compensatory effect among ankle, knee, and hip joints during the closed kinetic chain movement was observed in the previous literatures. Therefore, it is assumed that H:Q imbalance can cause non-contact lower extremity injuries without necessarily causing knee injuries. PURPOSE: To prospectively investigate the relationship between H:Q strength imbalance and overall non-contact lower extremity injuries. A prospective cohort study was conducted on NCAA division III basketball and soccer players during one season. METHODS: A total of eighty-two NCAA Division III athletes (41 female [19.56±1.34 yrs, 68.2±10.84 kg, 166.3±6.78 cm] and 40 male [19.97±1.43 yrs, 75.45±8.23 kg, 173.21±7.65 cm]) volunteered to participate in this study. Isokinetic dynamometer was used to assess H:Q strength balance at an angular velocity of 60°/s. RESULTS: The result of a chi-square test showed a trend (p < 0.05) that lower than 60% of H:Q was associated with non-contact leg injuries. CONCLUSION: This suggests that research and prevention strategies of athletic non-contact leg injuries require holistic examination. Consideration of bi-articulate nature of the lower leg muscles and compensatory roles between joints will give us more insights regarding non-contact leg injury mechanism and future preventative strategies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".