Combination of Curcuminoids and Acupressure for Inflammation and Pain in Older People with Osteoarthritis Genu: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Curcuminoids and acupressure have beneficial effects in reducing pain and inflammation in patients with osteoarthritis. However, only a few clinical trials are investigating biomarkers to prove this objectively. OBJECTIVE: This study aims to investigate the effect of acupressure and curcuminoids on inflammatory markers and pain in older people with osteoarthritis genu. METHODS: A randomized controlled trial (RCT) was conducted among older people with osteoarthritis. All participants were randomized to a group that received 30 mg of curcuminoids in turmeric extract capsules and acupressure (group 1) or a group that received a placebo and sham acupressure (group 2) for 3 weeks. RESULTS: The study was approved by the research ethics board; ClinicalTrials.gov reviewed this protocol. The extracts were manufactured from May 2023 to June 2023. Participant recruitment was conducted in September and October 2023; a total of 72 participants aged 60 years or older participated, of whom 75% (n=54) were female. Data were analyzed in April 2024, and dissemination of results is expected by the end of 2024. CONCLUSIONS: Primary outcomes were assessed at baseline and after the intervention. Relationships were assessed with inflammatory markers, endorphin hormones, and blood level of cycloxygenase-2 hormone. Additionally, secondary outcomes included pain, ability to perform activities of daily living, and quality of life. The beneficial effects that may be found in this trial may be exceptionally relevant in clinical practice, justifying this scientific inquiry. The benefits of herbs and acupressure can be helpful as additional options in treating inflammation and pain in patients with osteoarthritis. TRIAL REGISTRATION: ClinicalTrials.gov NCT06105840; https://clinicaltrials.gov/study/NCT06105840. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54970.
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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 | 0.038 | 0.038 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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".