Clinical Observation of Xingnao Kaiqiao Acupuncture Therapy Combined with Scalp Needle for Thalamic Pain after Stroke
Bibliographic record
Abstract
Objective: To observe the clinical effect on thalamic pain after stroke by using Xingnao Kaiqiao acupuncture and scalp needle therapy. Methods: 58 patients with thalamic pain were randomly divided into two group: 29 cases received the combined treatment with Xingnao Kaiqiao acupuncture therapy and scalp needle method; 29 cases in the control group were by scalp needle therapy. Four weeks after treatment all patients were assessed with the methods of Visual Analogue Score( VAS),McGill Pain Questionnaire( MPQ) and the clinically curative rate as evaluation index of curative effect. Results: After four weeks,of the 29 cases in the treatment group,7 cases were healed; 14 cases were markedly better; 6 cases were effective; 2 cases were invalid. The total effective rate was 93. 10%. Of the 29 cases in the control group,3 cases were healed; 6 cases were markedly better; 12 cases were effective; 8 cases were invalid. The total effective rate was 72. 41%. Compared between the two groups,there was a significant difference between the total effective rates( P 0. 05) and the effective constitution( P 0. 05). Conclusion: It suggests that the curative effect of combined treatment with Xingnao Kaiqiao acupuncture therapy and scalp needle method is better than that of the scalp needle acupuncture for the treatment of thalamic pain. It can significantly reduce the pain and improve the quality of life among patients.
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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) | 0.000 | 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".