An overview of interventions designed to reduce risk factors for sport injury
Bibliographic record
Abstract
Background While physical activity is associated with an overall reduction in mortality, morbidity and an improved quality of life, it also increases the risk of injury and illness. Objective To identify gaps in the literature with respect to interventions designed to reduce risk factors for sport injury. Design Review. Databases PubMed, Cinahl, Web of Science, Embase, and Sports Discus were searched. Setting All ages, sex, levels of competition and Olympic sports were included. Main outcome measurement Pattern of publications over time stratified by study design, intervention type, sport and anatomic site. Results Only 144 of 2525 articles retrieved by the search strategy met the inclusion criteria. Cross-over study designs increased by 175% since the late 1980's until 2005 but have declined 32% since. Randomised controlled trial (RCT) study designs increased by 650% since the early 1990's. Protective equipment was the focus of 61.8% of the studies and training the remaining 35.4%. Equipment research studied stability devices (83.1%) and attenuating devices (13.5%) while training research studied balance and coordination (54.9%), strength and power (43.1%) and stretching (15.7%). Almost 92.1% of the studies were of the lower extremity and 78.1% were of the joint (non-bone)-ligament type. 57.5% of the reports studied contact sports, 24.2% collision, and 25.75% non-contact sports. Conclusion We were surprised to find only 144 publications examining interventions designed to reduce risk factors for sport injury. The decrease in cross-over study design and increase in RCTs indicate a shift in research focus. Most notable was the finding that studies using equipment interventions have been decreasing since 2000 (35% decline) while those using training interventions have been increasing (213% increase).
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".