11pt, fleqn, a4paper, ]LegrandOrangeBook Effectiveness of a program for pain intensity and knee osteoarthritis reduction among elderly individuals in Thailand: A randomized controlled trial study
Bibliographic record
Abstract
Aim: This research aimed to evaluate the effects of the Pain Intensity and Knee Osteoarthritis Reduction (PIKOR) program on pain and severity of knee osteoarthritis (KOA) among elderly individuals in Thailand. Methods: This randomized controlled trial involving 58 older adults with KOA was conducted between October 2022 and February 2023 in a northern Thai province. The participants were randomly assigned to either an intervention group receiving the PIKOR program or a control group receiving routine treatment. The PIKOR program integrated four Thai traditional medicine treatments, Court-Type Traditional Thai Massage (CTTM), Hot Herbal Compress (HHC), Legs and Knee Stretching (LKS), and Knee Poultice by Herbs (KPH), along with a reduction in the time spent by a Thai traditional doctor. Treatment outcomes were assessed using the Visual Analog Scale (VAS) and the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) through independent and paired t-tests at a 95% significance level. Results: The participants in the intervention group exhibited a more significant reduction in VAS and WOMAC mean difference scores compared to those in the control group. Improvements in VAS and WOMAC scores were observed in the intervention and control groups; however, the differences between the groups were not statistically significant. Conclusion: The PIKOR program demonstrated more significant reductions in pain, stiffness, and functional limitations among elderly individuals in the intervention group than in the control group. It may serve as an alternative treatment for managing KOA, especially reducing the time spent with a traditional Thai doctor.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".