Association between the degree of Osteoarthritis and pain level of patients at Baptist Hospital, Batu City
Bibliographic record
Abstract
Osteoarthritis is a chronic disease characterized by the destruction of cartilage in the joints, causing stiffness, pain, and impaired movement. The condition most commonly affects the joints of the knees, hands, feet, spine, and often the shoulders and hips. Knee osteoarthritis is a major public health problem that causes chronic pain and reduces physical function and quality of life. This study determined the relationship between the degree of Osteoarthritis and the pain level in patients at Baptist Hospital, Batu City. This research used a cross-sectional study with 31 respondents. Data collection was conducted directly using the WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) questionnaire, modified in Indonesian. To determine the grade of Osteoarthritis using the Kellgren-Lawrence system. The results showed that out of 31 respondents, there were nine patients (29%) who had grade 1 osteoarthritis, 12 patients (38.7%) had grade 2, and 10 patients (32.3%) had grade 3. There were 22 patients (71%) who had a mild pain level, four patients (13%) had a moderate level, and five patients (16%) had a severe pain level. The Spearman correlation test showed no significant relationship (p>0.05) between the degree of Osteoarthritis and the level of pain in patients. This study concludes that there was no relationship between the increasing degree of Osteoarthritis and the level of pain.
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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 | 0.002 |
| 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 | 0.000 | 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".