Musculoskeletal Ultrasound Assessment of the Clinical Efficacy of the Combination of Acupressure and “Three Methods of Neck Movement (TCM)” Therapy in the Treatment of Cervical Spondylosis: A Study Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Jinhong Zuo,* Xiayang Zeng,* Hongyi Ma, Peng Chen, Xinlei Cai, Zhenyu Fan, Jianpeng Qu Massage Department, Zhejiang Hospital, Hangzhou City, People’s Republic of China*These authors contributed equally to this work and co-first authorsCorrespondence: Jianpeng Qu, Massage Department, Zhejiang Hospital, No. 12 Lingyin Road, Xihu District, Hangzhou City, 310000, People’s Republic of China, Email qjp0007@126.comBackground: Neck-type cervical spondylopathy (NTCS), a common degenerative disorder affecting the spine, poses challenges for patients and society. Research has demonstrated the effectiveness of traditional tuina techniques in treating NTCS, although some limitations still exist. Our study aimed to evaluate the effectiveness of combining regular massage techniques with three methods of neck movement (TCM) therapy for managing NTCS, utilizing musculoskeletal ultrasound measurements.Patients and Methods: In this study, 70 eligible patients with non-traumatic cervical spondylosis will be randomly assigned in a 1:1 ratio to either the experimental group, which will receive Tuina combined with a three-method neck movement treatment, or the control group, which will receive standard Tui Na manipulation. All participants will receive treatment for four weeks. Assessments will be conducted using musculoskeletal ultrasound, the McGill Pain Scale, and the Neck Disability Index (NDI) at three-time points: before treatment, at the end of treatment, and after 12 and 16 weeks of treatment.Conclusion: This paper investigates the utility of musculoskeletal ultrasound as a tool for evaluating the therapeutic efficacy of an integrated Traditional Chinese Medicine (TCM) strategy in alleviating pain and enhancing functional outcomes for patients with NTCS. The objective is to present a clinically viable and long-term treatment option.Trial Registration: Chinese Clinical Trial Registry, ChiCTR2300072648. Registered on June 20, 2023.Keywords: neck-type cervical spondylopathy, randomized controlled trial, therapeutic neck movement techniques, musculoskeletal ultrasonography
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".