Clinical Observation of MRI Image in Floating Needle Therapy for Cervical Spondylosis of Cervical Type
Post-publication record
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Bibliographic record
Abstract
In order to solve the problem of cervical spondylosis in the early stage of various cervical spondylosis, effective treatment can prevent the deterioration of the disease. This paper presents the results of a clinical trial examining magnetic resonance imaging in the treatment of cervical spondylosis with flotation therapy and selected 68 patients with cervical spondylosis. According to research commodity, using a rigorous randomized controlled trial, 34 cases were divided into a control group (acupuncture group). The needles were kept for 30 minutes once a day. The treatment group (acupuncture combined with floating acupuncture group) was treated with acupuncture on the 1st, 3rd, and 5th days and floating acupuncture on the 2nd, 4th, and 6th days, respectively. Both groups were treated for 6 consecutive days and rested for 1 day. After 2 weeks of treatment, the simplified McGill Pain Scale (MPQ), visual analogue scale (VAS), and neck pain scale (NPQ) were observed and recorded to compare the curative effects. Finally, Excel software is used to manage the data, and SPSS21.0 is used for statistical analysis. Measurements of gender, age, disease, VAS, simple MPQ, and NPQ of the two groups were compared in the two groups, P > 0.05 , which was not significant and comparable. After treatment, VAS, simple MPQ, and NPQ of the two groups were compared in and between groups, the total P < 0.05 , with the mean data. Topics. Acupuncture combined with float needle and acupuncture therapy can improve the pain and breathing of cervical spondylosis and improve the quality of life of patients, but acupuncture combined with needle float is more pronounced than acupuncture groups.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".