THE EFFECT OF TWO SPORT-SPECIFIC CLEAT PATTERNS ON PEAK PLANTAR PRESSURES DURING TWO RUNNING TASKS ON FIELDTURFTM
Bibliographic record
Abstract
The purpose of this investigation was to examine the effect of two sport-specific cleat patterns (used interchangeably on FieldTurf ) on peak plantar pressures during two running tasks (side cut and cross cut) on FieldTurfTM. Protocols were designed to determine if the turf-specific outsole effectively dispersed peak pressures on certain regions of the foot to a greater degree than a multi-stud outsole. This study was also used to determine if one shoe type would produce faster times during maximal effort sprint trials. Testing was performed on volunteer collegiate and amateur level football and soccer players from The University of Western Ontario. A pressure distribution measuring system for monitoring loads between the foot and the shoe known as the Pedar Mobile System was used in this study to measure peak pressure and maximum force exerted during the cutting motions. Differences between the testing conditions were determined using paired samples t-tests. The analyses demonstrated significant differences between the turf shoe and the multi-stud shoe in peak pressure during both the side cut and the cross cut. The turf-specific shoe was found to reduce the loads in both tasks. No difference was found in maximal sprint effort trials. While the clinical significance of the differences found requires further study, the present findings suggest that turf-specific cleats do, in fact, reduce peak pressure in the forefoot to a greater extent than other types of cleated footwear on FieldTurfTM
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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) | 0.001 | 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 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".