Bibliographic record
Abstract
Abstract Background: One of the most frequently injured muscle groups in soccer is the hamstring group. Soccer players have high rates of hamstring injury (HSI) due to frequent sprinting, changes in direction and similar high-risk activities. Such movements put immense stress on the hamstrings which can lead to injury. Over the last decade, at least four different HSI prevention programs and techniques have been explored in research. The purpose of this review is to assess the effectiveness of these methods. Methods: Studies in this review were collected using multiple database searches of PubMed. A total of six studies were selected, all either randomized controlled trials (RCTs) or cluster-RCTs. The selected trials were from various soccer leagues in Denmark, Japan, the Netherlands, and the United States. Results: The most prevalently studied method is the Nordic hamstring exercise (NHE). Studies that utilized the NHE, either as a stand-alone or within a program, reduced the risk of HSI by 15-71%. In addition, effective programs were characterized by progressively increasing the difficulty of exercises and high compliance rates. Conclusion: Studies that utilized the NHE showed effectiveness in the prevention of hamstring injuries in male soccer athletes. Based on the limited research available, it is unclear whether adding other exercises to prevention programs further reduces injury risk. More research is needed to explore new and existing prevention methods in a variety of populations and regions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".