Traditional and Complementary Medicine Use in Knee Osteoarthritis and its Associated Factors Among Patients in Northeast Peninsular Malaysia
Bibliographic record
Abstract
OBJECTIVES: We sought to determine the prevalence of traditional and complementary medicine (TCM) use for knee osteoarthritis and its associated factors among patients attending a referral hospital in an eastern coastal state of Malaysia. METHODS: This cross-sectional study included 214 patients with knee osteoarthritis. A universal sampling method was applied to patients who attended the outpatient clinic in Hospital Universiti Sains Malaysia from May 2013 to October 2013. Participants were given a questionnaire to determine their sociodemographic information and a validated Bahasa Malaysia version of the Western Ontario and McMaster Universities Arthritis Index (WOMAC). This questionnaire was used to assess the severity of knee osteoarthritis (i.e., pain, stiffness, and disturbances in daily activity). RESULTS: Over half (57.9%) of patients reported using TCM to treat knee osteoarthritis. Factors associated with TCM use were gender (odd ratio (OR) = 2.47; 95% confidence interval (CI): 1.28-4.77), duration of knee osteoarthritis (OR = 1.51; 95% CI: 1.03-2.23), and the severity of knee pain (OR = 2.56; 95% CI: 1.71-3.86). CONCLUSIONS: The prevalence of TCM use among eastern Malaysian patients with knee osteoarthritis was high. Physicians caring for these patients should be aware of these findings so that inquiries regarding TCM use can be made and patients can be appropriately counseled.
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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) | 0.001 | 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".