Application of five-element music therapy in pain coping skills training in patients with knee osteoarthritis
Bibliographic record
Abstract
Objective: The objective of this study is to assess the application effect of five elements music therapy introduced in the pain coping skills training of knee osteoarthritis (KOA). Materials and Methods: Totally, 80 patients with KOA were selected and randomly divided into the experimental group (39 cases) and the control group (41 cases). The control group was only given routine nursing measures, and the experimental group was additionally treated with five-element music therapy on the basis of the control group, twice a day, 28 days in total. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was used to evaluate the functional status of the knee joint of the two groups. The clinical efficacy of the two groups was evaluated by Guiding Principles for Clinical Research of New Chinese Medicine in the Treatment of Osteoarthritis. Results: WOMAC score statistically significantly decreased in the experimental group (35.92 ± 9.48 vs. 16.17 ± 5.43, P < 0.01) and the control group (36.73 ± 6.42 vs. 22.53 ± 7.51, P < 0.01) after 28 days of intervention when compared with that before intervention; WOMAC score in the experimental group was lower than that of the control group after 28 days of intervention (16.17 ± 5.43 vs. 22.53 ± 7.51, P < 0.01). The total effective rate of the experimental group was statistically higher than that of the control group (82.0% vs. 51.2%, χ 2 = 11.97, P = 0.003). Conclusion: The combination of five-element music therapy and routine nursing measures has better effect in relieving pain and bad emotions of patients with KOA when compared with routine nursing measures alone.
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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 | 0.000 | 0.000 |
| 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".