Side-sloped surfaces substantially affect lower limb running kinematics
Bibliographic record
Abstract
Running on side-sloped surfaces is a common obstacle in the environment; however, how and to what extent the lower extremity kinematics adapt is not well known. The purpose of this study was to determine the effects of side-sloped surfaces on three-dimensional kinematics of hip, knee, and ankle during stance phase of running. Ten healthy adult males ran barefoot along an inclinable runway in level (0°) and side-sloped (10° up-slope and down-slope inclinations, respectively) configurations. Right hip, knee, and ankle angles along with their time of occurrence were analysed using repeated measures MANOVA. Up-slope hip was more adducted (p = 0.015) and internally rotated (p = 0.030). Knee had greater external rotations during side-sloped running at heel-strike (p = 0.005), while at toe-off, it rotated externally and internally during up-slope and down-slope running, respectively (p = 0.001). Down-slope ankle had greatest plantar flexion (p = 0.001). Up-slope ankle had greatest eversion compared with down-slope (p = 0.043), while it was more externally rotated (p = 0.030). These motion patterns are necessary to adjust the lower extremity length during side-sloped running. Timing differences in the kinematic events of hip adduction and external rotation, and ankle eversion were observed (p = 0.006). Knowledge on these alterations is a valuable tool in adopting strategies to enhance performance while preventing injury.
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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 | 0.001 |
| 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) | 0.002 | 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".