Hamstring to quadriceps strength ratio and noncontact 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 strength ratio (HQR) 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 hamstrings and quadriceps (H:Q) imbalance can cause non-contact lower extremity injuries without necessarily causing knee injuries. The purpose of this study was 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. 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 which tested Q and H strength at 60°/s. A trend (p< 0.05) indicating that lower than 60% of HQR was associated with non-contact leg injuries was apparent. This suggests that the H:Q imbalance may be of significance in athletic non-contact leg injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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".