Prevention of sports injury I: a systematic review of applied biomechanics and physiology outcomes research
Bibliographic record
Abstract
OBJECTIVE: To analyse published articles that used interventions aimed at investigating biomechanical/physiological outcomes (ie, intermediate risk factors) for sport injury prevention in order to characterise the state of the field and identify important areas not covered in the literature. DATA SOURCES: PubMed, Cinahl, Web of Science and Embase were searched using a broad search strategy. MAIN RESULTS: Only 144 of 2525 articles retrieved by the search strategy met the inclusion criteria. Crossover study designs increased by 175% in the late 1980s until 2005 but have declined 32% since then. Randomised controlled trial (RCT) study designs increased by 650% since the early 1980s. Protective equipment studies (61.8% of all studies) declined by 35% since 2000, and training studies (35.4% of all studies) increased by 213%. Equipment research studied stability devices (83.1%) and attenuating devices (13.5%) whereas training research studied balance and coordination (54.9%), strength and power (43.1%) and stretching (15.7%). Almost all (92.1%) studies investigated the lower extremity and 78.1% were of the joint (non-bone)-ligament type. Finally, 57.5% of the reports studied contact sports, 24.2% collision and 25.8% non-contact sports. CONCLUSION: The decrease in crossover study design and increase in RCTs over time suggest a shift in study design for injury prevention articles. Another notable finding was the change in research focus from equipment interventions, which have been decreasing since 2000 (35% decline), to training interventions, which have been increasing (213% increase). Finally, there is very little research on overuse or upper extremity injuries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".