Effect of Acupuncture on the Cognitive Control Network of Patients with Knee Osteoarthritis: Study Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Purpose: Abnormal central nervous system function is the key central pathological factor leading to chronic pain in patients with knee osteoarthritis (KOA). Acupuncture can effectively relieve the pain of KOA patients. However, the central nervous mechanism of acupuncture treating KOA is not fully understood. This trial will use functional magnetic resonance imaging (fMRI) analysis techniques to investigate the potential central nervous mechanism of acupuncture treatment of KOA. Materials and Methods: A total of 108 patients will be randomized (in a 1:1:1 ratio) into three groups, this trial will include 4-week treatment, patients in groups A and B will receive 20 acupuncture and sham acupuncture sessions, respectively, patients in group C will not receive any intervention, and all patients will receive fMRI scans before and after the intervention. The Western Ontario and McMaster Universities Osteoarthritis Index score (WOMAC) will be the primary clinical outcome. Then, we will explore the functional changes of the cognitive control network (CCN) in the brains of KOA patients through whole brain functional connectivity (FC) analysis and seed-based functional connectivity (sFC) analysis. Pearson correlation coefficient will be used to analyze the relationship between the improved value of the clinical correlation scale and the change of fMRI data. Discussion: This trial will analyze the efficacy of verum acupuncture, sham acupuncture and the waiting-list for KOA and explore the activity of the CCN in three groups of patients by fMRI, so as to reveal the central nervous mechanisms of acupuncture in the treatment of KOA. Study Registration: This study is approved by the Ethics Committee of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine (No: 2019HL-133-01) and registered in the Chinese Clinical Trial Registry, ChiCTR2000038554.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".