The Effects of Applied Thai Traditional Massage Combined with Knee Exercise on Knee OA Patients: A Case Study of Ban Kracheng Community Health Promoting Hospital, Pathum Thani Province, Thailand
Bibliographic record
Abstract
The present study aimed to examine the effects of applied Thai traditional massage combined with knee exercise on knee osteoarthritis (OA) patients.To achieve the research objective, a randomized controlled trial was conducted.The data were collected from 31 knee OA patients using a survey questionnaire, a 10-level pain intensity assessment scale, the Western Ontario and McMaster Universities Osteoarthritis Index, and the Thai version of the Oxford Knee Score translated by the Royal College of Orthopaedic Surgeons of Thailand.Then the data were analyzed using descriptive statistics and the paired sample t-test.The results showed that the majority of the subjects were female aged 60 or over.After the administration of the treatment, almost three-fourths reported experiencing less severe OA and lower knee pain.Also, a pre-and post-treatment comparison revealed increased knee range of extension and flexion measured with a goniometer and improved quality of life at the significance level of 0.05.Based on the findings, it can be concluded that applied Thai traditional massage combined with knee exercise can effectively alleviate OA by relieving muscle contraction, enhancing blood circulation, and strengthening the knee joint.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".