Upaya Deteksi Dini Osteoarthritis pada Karyawan Universitas Tarumanagara dengan Instrumen WOMAC
Bibliographic record
Abstract
Osteoarthritis (OA) is a degenerative joint disease that commonly affects individuals in their productive and elderly years, significantly impairing quality of life. Early detection is essential to prevent disability due to OA, particularly in vulnerable groups such as university employees. This community service activity involved OA screening of 26 employees at Tarumanagara University using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). In addition to screening, participants received education on risk factors and OA prevention. The screening revealed varied levels of joint pain, stiffness, and functional limitations. The average WOMAC score was 32.27 (SD 17.63), ranging from 4 to 66 points. Several participants exhibited moderate to severe symptoms, especially during activities such as climbing stairs and performing household tasks. The WOMAC instrument proved effective in detecting early functional joint complaints. The educational component played a crucial role in raising participants’ awareness of the importance of healthy lifestyles and early OA management. This initiative successfully identified OA risks among university employees and enhanced their knowledge regarding disease prevention. Early detection and education should be established as routine workplace health programs. Keywords: Early Detection, Community Service, Osteoarthritis, University Employees, WOMAC
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".