The cerebral mechanism of the specific and nonspecific effects of acupuncture based on knee osteoarthritis: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Research on the effect of acupuncture has been limited. Whether the effect of acupuncture is equivalent to placebo has been the focus of debate in this field. This study will explore the specific and non-specific effects of acupuncture for knee osteoarthritis (KOA) by functional magnetic resonance imaging (fMRI). METHODS AND DESIGN: Ninety participants diagnosed with KOA will be randomly divided into the acupuncture group, sham acupuncture group, and waiting list group in a ratio of 1:1:1. Except for the waiting list group, the other participants will receive acupuncture or sham acupuncture three sessions per week for 4 weeks respectively. The primary outcome will be the response rate which is defined on an individual basis as at least a 2-point decrease in the numerical rating scale (NRS) of pain at the end of intervention period compared with the baseline. fMRI scans will be performed at baseline and the end of the intervention period to examine the response of various brain regions. The secondary outcomes will include the Western Ontario and McMaster Osteoarthritis Index (WOMAC), State-Trait Anxiety Scale-State Anxiety Subscale (STAI-S), and Stanford Expectations of Treatment Scale (SETS). Pearson's correlation coefficient will be performed to investigate the changes in brain activity and clinical variables. DISCUSSION: The results of our study will help to evaluate the specific and nonspecific effects of acupuncture combined with clinical and brain function changes based on KOA. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1900025799. Registered on 9 September 2019.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.010 |
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".