Clinical Observation on Efficiency of Warm-needling with Different Moxibustion Intensity for Patients with Cervical Spondylosis of Wind-cold Type
Bibliographic record
Abstract
Objective: To compare the efficiency of warm-needling with different moxibustion intensity in patients with cervical spondylosis of wind-cold type. Methods: Ninety patients with cervical spondylosis of wind-cold type were randomized into three groups: A group in which the patients were treated by warm-needling with one moxa cone,B group in which the patients were treated by warm-needling with three moxa cones, and C group in which the patients were treated by warm-needling with five moxa cones,with thirty cases in each group. All the patients were given the Neck Eight-needle therapy,meanwhile the Dazhui ( GV 14) was given warm-needling therapy. The pain and the function of cervical vertebra were evaluated by Short-form of McGill Pain Questionnaire and Assessment Scale for Cervical Syndrome Index respectively. Results: ①Pain: After treatment,the scores of pain rating index ( PRI) ,visual analogue scale ( VAS) and present pain intensity ( PPI) in three groups were obviously decreased ( P 0. 05,P 0. 01) ,and the scores of PRI,VAS and PPI in B and C groups were lower than those in A group ( P 0. 05) ,while there was no statistical difference between B group and C group on the scores ( P 0. 05) . ②Function of cervical vertebra: After treatment,the scores of cervical vertebra function in three groups were significantly increased ( P 0. 05,P 0. 01) ,and the scores of B and C groups were higher than those of A group ( P 0. 05) ,while there was no statistical difference between B group and C group on the scores of cervical vertebra function ( P 0. 05) . Conclusion: Warm-needling with three moxa cones can ensure a significant effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".