Correlation of Foot Posture with Balance and Pelvic Tilt in Healthy Runners
Bibliographic record
Abstract
Introduction: Hyper-pronated foot is a functional deformity which mainly affects the total body kinematic chain during dynamic weight-bearing events such as running when the foot lands on the ground. Furthermore, individuals with hyper-pronated foot may exhibit anterior pelvic tilt owing to the biomechanical relations, which alters balance as well. Runners with hyper-pronated feet are at high risk of injury, possibly because of larger torque generated at the lower limb. Objective: The purpose of the study was to find out the relationship of hyper-pronated foot with anterior pelvic tilt and dynamic balance in recreational runners. Methods: A cross-sectional study was conducted in 55 healthy recreational runners with hyper-pronated foot aged 19-30 years. They were assessed by foot posture index for hyper-pronated foot, Star Excursion Balance Test for dynamic balance evaluation, and the photogrammetry method to determine the anterior pelvic tilt angle. Results: The results revealed a poor correlation between foot posture index and dynamic body balance (r = 0.23) and a moderate correlation between foot posture index and anterior pelvic tilt angle (r = 0.47). Conclusions: There was no significant correlation of foot posture index with dynamic body balance, whereas a minimal correlation was found between foot posture index and the anterior pelvic tilt angle. Therefore, hyper-pronated foot does not significantly directly influence balance or posture.
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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 | 0.001 | 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) | 0.003 | 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".