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 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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".