Weight Bearing Versus Non-Weight Bearing Exercises for Patients with Knee Osteoarthritis
Bibliographic record
Abstract
Background: The most prevalent progressive musculoskeletal disorder that can impact joints is osteoarthritis (OA). Knee biomechanics is significantly impacted by periarticular muscle weakening. Exercises involving weight bearing have been demonstrated to improve lower extremity neuromuscular control and muscle strength. However, non-weight-bearing workouts increase knee stability and may lessen discomfort. Objective: the study was conducted to investigate the effects of weight bearing exercises and non-weight bearing exercises on patients with mild to moderate knee osteoarthritis (KOA). Methods: Thirty-six patients with KOA. Age of patients ranges from 50 – 60 years old. The Western Ontario and McMaster Universities (WOMAC) questionnaire in Arabic was used to measure the results of osteoarthritis, and the Handheld Dynamometer (HHD) was used to measure the strength of the knee extensor and abductor, pain intensity was measured by Visual Analogue Scale (VAS), physical capacity was measured by 6-minute walk test (6MWT). They were randomized into two groups,Group I (non-weight bearing exercises) while Group II (weight bearing). Results: Both the weight-bearing and the non-weight-bearing exercise groups experienced a considerable decline in WOMAC and VAS, with no statistically significant difference between the two groups. Both groups showed a significant gain in muscle strength and 6MWT, with no statistically significant difference between the two groups. Conclusion: Both weight bearing and non-weight bearing exercises groups showed significant improvements within their respective interventions, no statistically significant difference was found between the two approaches in terms of decreasing pain, function, and increasing muscle strength in patients with 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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".