Cerebral mechanism of Tuina on the descending pain inhibitory system in knee pain Study protocol for a randomized controlled parallel trial
Bibliographic record
Abstract
Abstract Background: Tuina, a manual therapy, is effective and safe for reducing clinical symptoms of knee osteoarthritis (KOA); however, the mechanism that influences pain through the descending pain inhibitory system in KOA is unclear. Thus, we will investigate the modulatory implications of Tuina on the rostral ventromedial medulla (RVM) and periaqueductal gray (PAG), which have critical roles in the descending pain inhibitory system in patients with KOA. Methods: This is a randomized, controlled parallel trial. Patients with KOA will be randomly assigned (1:1) to 6 weeks of health education or Tuina. Functional and structural magnetic resonance imaging, pressure pain thresholds, numerical rating scale, Hamilton Anxiety Scale, Western Ontario and McMaster Universities Osteoarthritis Index, and Hamilton Depression Scale will be conducted at the beginning and end of the experiment. We will use PAG and RVM as seeds in resting-state functional connectivity (rsFC) analysis. Adverse events will be documented and assessed throughout. The outcome evaluators and data statisticians will be blinded to the treatment group assignment to reduce the risk of bias. Discussion: Our trial will provide evidence on the effect of Tuina on rsFC in patients with KOA and identify possible relationships between rsFC changes and improvement of clinical variables, elucidating the effect of Tuina on the descending pain inhibitory system of patients with KOA. Trial registration: Chinese Clinical Trial Registry (ChiCTR2300070289). Date of registration: April 7, 2023
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".