Footwear Traction and Lower Extremity Noncontact Injury
Bibliographic record
Abstract
PURPOSE: Football is the most popular high school sport; however, it has the highest rate of injury. Speculation has been prevalent that foot fixation due to high footwear traction contributes to injury risk. Therefore, the purpose of the study was to determine whether a relationship exists between the athlete's specific footwear traction (measured with their own shoes on the field of play) and lower extremity noncontact injury in high school football. METHODS: For 3 yr, 555 high school football athletes had their footwear traction measured on the actual field of play at the start of the season, and any injury the athletes suffered during a game was recorded. Lower extremity noncontact injury rates, grouped based on the athlete's specific footwear traction (both translational and rotational), were compared. RESULTS: For translational traction, injury rate reached a peak of 23.3 injuries/1000 game exposures within the midrange of translational traction, before decreasing to 5.0 injuries/1000 game exposures in the high range of traction. For rotational traction, there was a steady increase in injury rate as footwear traction increased, starting at 4.2 injuries/1000 game exposures at low traction and reaching 19.2 injuries/1000 game exposures at high traction. CONCLUSIONS: A relationship exists between footwear traction and noncontact lower extremity injury, with increases in rotational traction leading to a greater injury rate and increases in translational traction leading to a decrease in injury. It is recommended that athletes consider selecting footwear with the lowest rotational traction values for which no detriment in performance results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".