The influence of sports-shoes mechanical properties on the frequency of lower-extremity athletic injuries
Bibliographic record
Abstract
Summary During a full basketball season a study was performed to determine the influence of the shoe torsional stiffness on the injury frequency of lower-extremity. Four groups of 40 semi-professional players tested an adidas basketball shoe that was identical per impression but it had four different torsional stiffnesses. Questionnaires before and after the season evaluated the medical history and injury frequency. Apart from the questionnaires a footprint of every subject was taken to evaluate the foot morphology. Results A statistic significant correlation of instability and rear-foot fit was found among the four groups (p=0.01). Within the different groups existed a correlation of body weight, stiffness and instability. Subjects with a higher body weight (>85kg) had a benefit in a higher stiffness (element 3 and 4) and subjects with lower body weight ( Conclusion A bodyweight adapted shoe-stiffness may maximize stability and comfort.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".