Efektivitas Intervensi Fisioterapi dalam Mengurangi Nyeri dan Meningkatkan ROM pada Lansia Penderita Osteoarthtritis Genu di RST. Soedjono Magelang
Bibliographic record
Abstract
Background: Knee osteoarthritis is one of the most common degenerative conditions in older adults, often causing pain and limited range of motion (ROM), which negatively affects quality of life. Physiotherapy management plays an important role in reducing pain, improving flexibility, and maintaining joint function. Objective: This study aims to evaluate the effectiveness of physiotherapy interventions in reducing pain and improving ROM in elderly patients with knee osteoarthritis. Method: A case study was conducted on a 63-year-old patient at Dr. Soedjono Level II Hospital, Magelang, who underwent three therapy sessions consisting of Infrared (IR), Transcutaneous Electrical Nerve Stimulation (TENS), Proprioceptive Neuromuscular Facilitation (PNF) with the hold-relax technique, and static cycling. Assessments were carried out using the Numeric Rating Scale (NRS), Likert Gait Scale (LGS), and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Results: The findings showed a significant reduction in pain on palpation (from 4/10 to 1/10), improvement in knee flexion ROM (from 110° to 135°), and functional enhancement indicated by a decrease in WOMAC scores from 39.58% to 27.08%. Conclusion: These results suggest that structured physiotherapy interventions provide therapeutic benefits in reducing pain, improving joint flexibility, and enhancing functional ability in elderly 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 | 0.000 |
| 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) | 0.004 | 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".