STRETCHING HAMSTRING EXERCISE DAN STRENGTHENING QUADRICEP MUSCLE EXERCISE TERHADAP PENINGKATAN AKTIVITAS FUNGSIONAL PENDERITA OSTEOARTHTRITIS GENU
Bibliographic record
Abstract
In the population of developing countries, moderate or severe disability due to osteoarthritis is 10 percent, while in the population of low-income countries it reaches 33.5%, and disability due to arthrosis occupies 43.4% of the world's population. Osteoarthritis affects about 21% of adults (46.4 million people) in the United States, and this number is expected to increase to 67 million by 2030. Hamstring stretching exercises are very effective at increasing the flexibility of muscles and joints, thereby reducing or eliminating joint pain. This exercise can also improve circulation and strengthen bones. Strengthening the quadriceps muscles helps reduce pain, improves body function and quality of life for patients and slows disease progression. WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) is an index used to assess the condition of patients with osteoarthritis of the knee. Questions consisted of pain, stiffness, physical and social functioning. This study used a pre-experimental method with a pre-test and post-test research design that aimed to determine the effect of giving stretching hamstring exercise and strengthening quadriceps muscle exercise. The conclusion is that there is an effect of stretching hamstring exercise and strengthening quadriceps muscle exercise on increasing the functional activity of patients with osteoarthritis genu
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".