Efficacy of warming needle moxibustion in the treatment of ankylosing spondylitis
Bibliographic record
Abstract
BACKGROUND: Ankylosing spondylitis is a recurrent autoimmune disease, which has a high disability rate and seriously affects patients' daily life. Conventional treatment cannot effectively solve the clinical problems of patients, and long-term medication is accompanied by adverse reactions. The evidence shows that warming needle moxibustion has advantages in the treatment of ankylosing spondylitis, but there is still a lack of clinical studies on warm acupuncture alone and long-term follow-up. METHODS: This is a prospective randomized controlled trial to study the efficacy and safety of needle warming through moxibustion in the treatment of ankylosing spondylitis. It was approved by the Ethics Committee of Clinical Research of our hospital. Patients were randomly assigned to an observation group or a control group. The patients were followed up for 6 months after 30 days of treatment. Observation indicators include; activity index, functional ability, Bath Ankylosing Spondylitis Metrology Index, inflammatory indicators, adverse reactions, and so on. Finally, SPASS 22.0 software is used for statistical analysis of the data. DISCUSSION: This study will evaluate the clinical efficacy of warming needle moxibustion in the treatment of ankylosing spondylitis. The results of this study will provide a reference basis for the clinical use of warm needle moxibustion in the treatment of ankylosing spondylitis. TRIAL REGISTRATION: OSF Registration number: DOI 10.17605/OSF.IO/GWPX3.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".