Hamstring stiffness and injury risk factors during the handball season in female players
Bibliographic record
Abstract
Monitoring the muscle mechanical properties and functions of female athletes throughout their training season is relevant to understand the relationships between these factors and to predict noncontact injuries, which are prevalent among female athletes. The first aim of this study was to determine whether female handball players’ passive stiffness of the hamstring muscles is associated with hamstring extensibility, strength of knee flexors and extensors, and lower limb stiffness. Additionally, the study monitored fluctuations in these factors over 25 weeks. The study utilized an isokinetic dynamometer to record hamstring passive stiffness, extensibility, and hamstring and quadriceps strength of 18 young handball players. Lower limb stiffness was determined from a countermovement vertical jump conducted on a force plate. The countermovement jump involved the calculation of the peak force during the eccentric phase and the mean force during the concentric phase. The results showed a positive correlation between hamstring passive stiffness and lower limb stiffness ( r = 0.660, p < 0.01), knee flexion and extension strength ( r = 0.592, p < 0.01 and r = 0.497, p < 0.05, respectively), and eccentric peak force ( r = 0.587, p < 0.01) during jumping. The strength of knee extensors increased significantly after 6 weeks, and hamstring stiffness after 12 weeks of training. In conclusion, the increased hamstring stiffness following training did not match other factors associated with injury risk. Therefore, preventing multifactorial injury risk requires a comprehensive approach, and monitoring one factor alone is insufficient to predict noncontact injuries in female handball players.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".