The effect of combined balance and strength exercise program in patients with different grades of primary knee osteoarthritis
Bibliographic record
Abstract
Abstract Background Knee osteoarthritis (KOA) is a degenerative disease that affects all parts of the joint including the surrounding ligaments, tendons, and muscles. Biomechanical changes that occur in KOA cause aggravation of symptoms with further joint damage. Thus, modifying the biomechanics of the knee joint may help in the prevention and treatment of KOA. For that reason, our aim was to assess the effect of combined balance and strengthening exercise programs in patients with different grades of primary KOA. Results All studied groups showed comparable significant improvement in quadricep muscle strength, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score (< 0.001), time-up and go score (< 0.001), 6-m walk time (< 0.001), and dynamic balance (< 0.001) at the end of exercise program. Furthermore, patients with mild-moderate KOA showed a significant improvement in pain, physical function, total WOMAC scores, and dynamic balance compared to those with more severe KOA. Conclusion Combined balance and strengthening exercise programs may help improve pain, physical function, and dynamic balance in patients with KOA regardless of its severity. However, following exercise patients with milder forms of KOA may show greater improvement compared to patients with severe KOA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".