The Effectiveness of Tuina in Relieving Pain, Negative Emotions, and Disability in Knee Osteoarthritis: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of Tuina in relieving the pain, negative emotions, and disability of patients with knee osteoarthritis (KOA). DESIGN: Single-center, parallel, randomized controlled trial. SETTING: Shanghai Guanghua Integrated Chinese and Western Medicine Hospital, Shanghai, China. SUBJECTS: Adult patients with KOA who were able to speak Chinese and self-report symptoms were eligible. METHODS: A total of 104 patients were randomly allocated to receive the 6-week treatment of Tuina (Tuina group) or celecoxib (celecoxib group). Data on pain, negative emotions, and disability were collected at baseline, at week 2, 4, and 6, and follow-up (1 month after the last treatment). The primary outcomes were the pressure pain thresholds. The secondary outcomes were: (1) numerical rating scale at rest and with movement; (2) Hamilton Anxiety Scale; (3) Hamilton Depression Scale; (4) Western Ontario and McMaster Universities Osteoarthritis Index; and (5) clinical effective rate. The adverse events of the trial were evaluated. RESULTS: In total, 99 patients completed the follow-up. Generalized linear mixed models were constructed to analyse the between-group differences. Statistically significant differences were found in the interaction effects (P < .05). In evaluating the group effect, statistical differences were found at week 6 and follow-up (P < .05). Further, all variables showed a time effect (P < .05). A statistical difference in the clinical effective rate was found between the Tuina and celecoxib groups (P < .05). CONCLUSIONS: Tuina produced superior effects for pain, negative emotions, and disability over time, as compared to celecoxib in patients with KOA.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".