Gait analysis of patients with knee osteoarthritis before and after Chinese massage treatment
Bibliographic record
Abstract
The objective of this study was to evaluate the effectiveness of Chinese massage therapy in patients with knee osteoarthritis (OA) by measuring lower-limb gait parameters. We recruited 20 women with knee OA, who then underwent Chinese massage therapy three times per week for 2 weeks. The patients underwent gait evaluation using a six-camera infrared motion analysis system. They completed Western Ontario and McMaster Universities Osteoarthritis Index questionnaires before and after treatment. We calculated the forward speed, step width, step length, total support time percentage, initial double support time percentage, and single support time percentage. We also measured the angles at the knee, hip, and ankle during the stance phase of walking. The results showed statistically significant mean differences in knee pain relief, alleviation of stiffness, and physical function enhancement after therapy (P < 0.05). The patients gained significantly faster gait speed, greater step width, and increased total support time percentage after the Chinese massage therapy (P < 0.05). There were no significant differences in the range of motion or initial contact angles of the knee, hip, or ankle during the stance phase of walking. We concluded that Chinese massage is a beneficial complementary treatment and an alternative therapy choice for patients with knee OA for short-term pain relief. Chinese massage may improve walking ability for these patients.
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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 | 0.001 | 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".