Efficacy and safety of acupuncture for hand osteoarthritis: study protocol for a multi-center, randomized, sham-controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: Hand osteoarthritis (OA) is a prevalent disorder in the general population. Patients with hand OA often report symptoms of pain, stiffness, and functional limitations, which cause clinical burden and impact on quality of daily life. However, the efficacy of current therapies for hand OA is limited. Other therapies with better effects and less adverse events are in urgent need. Acupuncture is well known for analgesia and has been proved effective in treating basal thumb joint arthritis. This study aims to clarify the efficacy and safety of acupuncture treatment for clinical symptomatic improvement of hand OA. METHODS: This will be a sham-controlled, randomized, multi-center clinical trial. A total of 340 participants will be recruited and randomly allocated to either traditional acupuncture group or sham acupuncture group. All participants will receive 12 treatment sessions over 4 weeks and 2 follow-up assessments in the following 3 months at week 8 and week 16. The primary outcome will be the proportion of responders at week 5. Secondary outcomes will include visual analog scale, Australian Canadian Osteoarthritis Hand Index, Functional Index for hand OA, the number of symptomatic joints, hand grip strength and pinch strength, global assessment, the World Health Organization Quality of Life abbreviated version and expectations. Safety will be evaluated during the whole process of the trial. All outcomes will be analyzed following the intention-to-treat principle. DISCUSSION: This prospective trial will provide high-quality evidence on evaluating the efficacy and safety of acupuncture treatment for hand OA. Results of this trial might contribute in offering a new option to clinical recommendations. Trial registration ClinicalTrials.gov Identifier: NCT05267093. Registered 23 February 2022.
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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 | 0.033 | 0.030 |
| 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 | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".