Association of Navicular Drop and Selected Lower-Limb Biomechanical Measures During the Stance Phase of Running
Bibliographic record
Abstract
There is evidence to suggest that navicular drop measures are associated with specific lower-extremity gait biomechanical parameters. The aim of this study was to examine the relationship between navicular drop and a) rearfoot eversion excursion, b) tibial internal rotation excursion, c) peak ankle inversion moment, and d) peak knee adduction moment during the stance phase of running. Sixteen able-bodied men having an average age of 28.1 (SD=5.30) years, weight of 81.5 (SD=10.40) kg, height of 179.1 (SD=5.42) cm volunteered and ran barefoot at 170 steps/minute over a force plate. Navicular drop measures were negatively correlated with tibial internal rotation excursion (r=-0.53, P=.01) but not with rearfoot eversion excursion (r=-0.19; P=.23). Significant positive correlations were found between navicular drop and peak knee adduction moment (r=.62, P<.01) and peak ankle inversion moment (r=.60, P<.01). These findings suggest that a low navicular drop measure could be associated with increasing tibial rotation excursion while high navicular drop measure could be associated with increased peak ankle and knee joint moments. These findings indicate that measures of navicular drop explained between 28% and 38% of the variability for measures of tibial internal rotation excursion, peak knee adduction moment and peak ankle inversion moments.
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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 | 0.004 |
| 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) | 0.001 | 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".