Thunder-fire moxibustion for cervical spondylotic radiculopathy: Study protocol for a randomized controlled trial
Bibliographic record
Abstract
Abstract Background Thunder-fire moxibustion originated in China and contains traditional Chinese medicine. It can produce strong firepower, infrared thermal radiation, and medicinal effects when burning on the acupoints. Thunder-fire moxibustion is commonly used in patients with neck pain, but its efficacy has rarely been systematically demonstrated. We designed a randomized trial of thunder-fire moxibustion on cervical spondylotic radiculopathy (CSR) to investigate whether it is more effective than ibuprofen sustained-release capsules. Methods One hundred patients will be recruited and randomly divided into thunder-fire moxibustion and ibuprofen groups. The intervention consists of ten treatments and will last for 2 weeks. The Yasuhisa Tanaka 20 Score Scale is used as the primary outcome measure. It contains a combination of the self-conscious symptom in patients, objective clinical evaluation from doctors, and social evaluation (the ability to work and live). The objective and comprehensive evaluation of CSR patients before and after treatment is particularly needed. The Short-Form McGill Pain Questionnaire-2 (SF-MPQ-2), Neck Disability Index score scale (NDI), and the Quality of Life Assessment (SF-36) are applied as secondary outcome measures. The assessment will take place at the baseline and the first and second weekends of treatment. If an adverse event (AEs) occurs, it will be reported. Discussion The aim of this trial is to determine whether thunder-fire moxibustion is more effective than ibuprofen in the treatment of patients with CSR. Trial registration Chinese Clinical Trial Registry ( http://www.chictr.org.cn ), ChiCTR1800018820 . Registered on 11 October 2018.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.075 | 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".