Medially wedged foot orthoses generate greater biomechanical effects than thin-flexible foot orthoses during a unilateral drop jump task on level and inclined surfaces
Bibliographic record
Abstract
BACKGROUND: Foot orthoses are therapeutic insoles designed to induce various effects on lower limb biomechanics. However, conflicting findings in previous research, highlight the need to better understand how foot orthoses with different features affect lower limb biomechanics during challenging tasks, particularly during unilateral drop jump landings. METHODS: Seventeen participants with flat feet were recruited to participate in this cross-sectional descriptive study that examined the effects of thin-flexible foot orthoses and medially wedged foot orthoses on lower limb biomechanics during unilateral drop jump landings on level and valgus inclined surfaces. Midfoot, ankle, knee, and hip angles and moments were calculated and compared across conditions with repeated measures ANOVAs, using a statistical parametric mapping approach. FINDINGS: Medially wedged and thin-flexible foot orthoses reduced ankle pronation and arch flattening during unilateral drop jump landings on level and valgus inclined surfaces. Medially wedged foot orthoses further decreased midfoot dorsiflexion and ankle eversion angles compared to thin-flexible foot orthoses. Medially wedged foot orthoses also generated greater effects on ankle kinetics and hip kinematics during unilateral drop jump landings. INTERPRETATION: Medially wedged foot orthoses are more effective than thin-flexible foot orthoses in optimizing lower limb biomechanics during unilateral drop jump landings. While the biomechanical effects did not increase on inclined surfaces, medially wedged foot orthoses generated greater effects on proximal joints, highlighting their potential to improve hip stability and enhance overall lower limb function. Personalized foot orthoses selection based on specific biomechanical profiles should be further explored to optimize orthotic interventions benefiting individuals with musculoskeletal conditions.
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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) | 0.003 | 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".