Osteoarthritic Tibiofemoral Joint Contact Characteristics During Weightbearing With Arch-Supported and Standalone Lateral Wedge Insoles
Bibliographic record
Abstract
Imbalanced joint load distribution across the tibiofemoral surface is a risk factor for osteoarthritic changes to this joint. Lateral wedge insoles, with and without arch support, are a form of biomechanical intervention that can redistribute tibiofemoral joint load, as estimated by external measures of knee load. The objective of this study was to examine the effect of these insoles on the internal joint contact characteristics of osteoarthritic knees during weightbearing. Fifteen adults with tibiofemoral osteoarthritis underwent magnetic resonance imaging of the affected knee, while standing under 3 insole conditions: flat control, lateral wedge alone, and lateral wedge with arch support. Images were processed, and the surface area and centroid location of joint contact were quantified separately for the medial and lateral tibiofemoral compartments. Medial contact surface area was increased with the 2 lateral wedge conditions compared with the control (P ≤ .012). A more anterior contact centroid was observed in the medial compartment in the lateral wedge with arch support compared with the lateral wedge alone (P = .009). Significant changes in lateral compartment joint contact outcomes were not observed. These findings represent early insights into how loading at the tibiofemoral interface may be altered by lateral wedge insoles as a potential intervention for knee osteoarthritis.
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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) | 0.001 | 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".