Clinical Research on Different Interval in Treating the Pain of Cervical Spondylosis with Acupuncture
Bibliographic record
Abstract
Objective:To find out a better interval for treating the pain of cervical spondylosis through the clinical research on different interval in treating the pain of cervical spondylosis with acupuncture.Methods:60 cases were randomly divided into Group 1 and Group 2 equally,treating them with acupuncture every other day and everyday respectively in weekdays.The curative effect was evaluated by NPQ and McGill scale.Results:The effective rate of Group 1 is 78.57% and Group 2 is 74.07%.There is no significant(P0.05)difference.In Group 1,the scores of NPQ and McGill significantly decrease(P0.01) form the beginning to the 6th time and from the 6th time to the end of the course.The scores of NPQ and McGill significantly decrease(P0.01)in Group 2 at the end of the course.At the end of the 2nd week,there is no significant difference(P0.05) between Group 1 and Group 2 for the scores of NPQ and McGill.Conclusion:Treating the pain of cervical spondylosis with acupuncture is a safe and effective method.In addition,treating the pain of cervical spondylosis with acupuncture everyday or every other day for 10 times are both effective.The treatment of every other day is more effective.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".