Early Detection and Strengthening Exercise Program for Knee Osteoarthritis and Fall Risk among Elderly Women in Gulingan Village, Bali
Bibliographic record
Abstract
The decline in estrogen hormone levels among elderly women who have entered the menopausal phase contributes to a deterioration in their overall health condition. One of the prominent effects is a reduction in muscle performance, which in turn leads to decreased muscle strength and diminished balance control. This community service program was designed with the following objectives: (1) to enhance elderly women’s understanding of the symptoms of knee osteoarthritis, its prevention strategies, management approaches, and the associated risk of falls; (2) to conduct preventive health efforts through early detection of knee osteoarthritis using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire and goniometer, and early detection of fall risk using the Timed Up and Go (TUG) test; and (3) to provide a lower-limb muscle strengthening exercise program. The activity was attended by 52 elderly women aged over 60 years residing in Gulingan Village in July 2025. The results of the knowledge regarding knee osteoarthritis and fall risk increased from 32,7% to 82,7%. Early detection using the WOMAC questionnaire revealed that the majority of participants were at a mild risk of knee osteoarthritis (80.78%). Goniometer measurements indicated that most participants maintained a normal range of joint motion. Furthermore, early detection using the TUG test demonstrated that the majority of elderly women had a low risk of falls (80.8%). This program successfully increased awareness and preventive behavior among elderly women, highlighting the importance of early screening and home-based strengthening exercise.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".