Efficacy of Combining Whole-body Vibration Training and Closed Kinetic Chain Exercises in Early Knee Osteoarthritis: A Preliminary Study
Bibliographic record
Abstract
Objectives: We aimed to conduct a preliminary evaluation of the effectiveness of integrating whole-body vibration training (WBVT) into conventional closed kinetic chain (CKC) exercises as an intervention strategy for early knee osteoarthritis (KOA). Methods: : This non-randomized comparative study conducted at an orthopedic clinic involved 53 patients (with Kellgren-Lawrence grades 1-2); 37 patients received only physical therapy (CKC group), and 16 patients received both physical therapy and WBVT (WBVT group). The primary outcome was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) subscale score for pain, and the secondary outcomes were the WOMAC subscale score for physical function and muscle-strength assessments [isometric knee extension strength and the 30-second chair-stand test (CS-30) score]. Propensity score matching (PSM) was used to adjust for bias between the control and intervention groups. Results: : After PSM adjustment, 13 patients were selected from each group. The WBVT group showed a significant improvement in the WOMAC pain score (d=1.16, P=0.007) and a significant increase in the CS-30 score (d=0.81, P=0.049). However, for the WOMAC physical function score, the between-group difference remained statistically insignificant (d=0.59, P=0.146). Conclusions: : WBVT may be effective in reducing the pain of early KOA. WBVT is a non-invasive and convenient method, underscoring its potential as a novel therapeutic option.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".