Effects of Lateral Wedge Insole on Static and Dynamic Balance in Patients with Moderate Medial Knee Osteoarthritis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Knee osteoarthritis (OA) is associated with diminished joint proprioception, balance deficits and increased risk of falls. Patients with medial knee osteoarthritis are at higher risk of balance deficits and falling due to the development of secondary varus deformity. Therefore, it is of great clinical importance to identify the treatment methods that could improve balance in knee OA. The purpose of this study was to evaluate the effects of Lateral Wedge Insole (LWI) on static and dynamic balance in patients with moderate medial knee osteoarthritis. METHODS: Thirty patients with moderate medial knee OA participated in this semi-experimental interventional study. Patients were recruited from orthopedic and rheumatology clinics of the university. For each patient, the static and dynamic balance were assessed in two conditions including wearing shoes with flat insole and wearing shoes with lateral wedge insole, using a Prokin stabilometer. The balance variables were the anteroposterior and mediolateral velocity and standard deviation of the center of pressure (COP) displacement, and total stability indices. Patients performed two familiarization trials, then, three actual trials with at least 30 s interval for each test. FINDINGS: Wearing LWIs resulted in significant decreases in the anteroposterior (static:1.78mm/s, dynamic:6.63mm/s) and mediolateral velocity of COP (static:1.53mm/s, dynamic:5.58mm/s) in compression to wearing shoes with flat insole (p<0.001). The anteroposterior (static:1.04 mm, dynamic:1.57mm) and mediolateral SD of the COP (static:1.58mm, dynamic:2.02mm) were also decreased after using LWIs (p<0.007). Except for the anteroposterior stability index, other balance indices were also significantly improved (p<0.05). CONCLUSION: Our findings suggest that using a LWI improve static and dynamic balance in patients with moderate medial knee OA.
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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".