Functional Profile of Patients with Knee Osteoarthritis at the Physical Medicine and Rehabilitation Clinic of Sanjiwani General Hospital Gianyar
Bibliographic record
Abstract
Introduction: Osteoarthritis (OA) is the most common form of arthritis worldwide, affecting millions and representing a leading cause of disability, particularly among the elderly. In Indonesia, the number of OA cases has more than doubled from 1990 to 2019. However, data on osteoarthritis cases in Bali remain limited. OA is a major cause of joint pain, swelling, and stiffness, leading to decreased quality of life. Objective: This study aims to explore the clinical profile and functional status of patients with knee osteoarthritis to provide a foundational database for future management. Methods: This was a descriptive observational study using a cross-sectional design. Data were collected during a single visit of patients diagnosed with knee OA to the Physical Medicine and Rehabilitation Outpatient Clinic at Sanjiwani General Hospital, Gianyar. Functional status was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire. Results and Discussion: Most of subjects were aged ≥61 years and predominantly female. Body Mass Index (BMI) ranged from normal to overweight. Most patients worked in the private sector. The WOMAC scores mostly fell within the mild category. Conclusion: The study found that patients with knee OA generally exhibited mild functional impairment, with minimal disruption to daily activities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".