Influence of Compliance and Aging of Artificial Turf Surfaces on Lower Extremity Joint Loading
Bibliographic record
Abstract
Background: Artificial turf (AT) has been related to increased injury rates when compared to natural grass (NG). One potential reason for the differences in injury rates is the difference in mechanical characteristics of the surfaces. Over the course of a season on artificial turf, due to heavy use and environmental factors, properties of the surface (such as compliance) may be altered. The purpose was to compare the effects of newly installed versus aged AT on injury risks at the metatarsophalangeal, ankle, and knee joint during soccer-specific movements. Methods: Eleven male soccer players performed three movements on newly installed and ‘aged’ AT. Kinematics and kinetics were collected for the different surfaces. Results: Knee adduction moments were increased during the v-cut (119 Nm vs. 164 Nm, p = 0.02), and knee external rotation joint moments were increased during the circle run (23 Nm vs. 28 Nm, p = 0.04) with the aged surface. No surface effects were seen during the jog-sprint transition. Conclusions: For movements associated with a high risk for non-contact injuries, the age of the AT resulted in greater risk factors for injury potential at the knee joint. Further research comparing injury rates associated with AT should consider mechanical features, specifically surface compliance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".