Dachangshu Deep Acupuncture Treatment of Lumbar Disc Herniation Clinical Observation
Bibliographic record
Abstract
Objective:To explore the acupuncture treatment of lumbar disc herniation mainly effects of the acupuncture points.Methods:82 patients were randomly divided into treatment group 45 cases and 37 cases of the control group.The two groups using acupuncture,traction,massage combined therapy,the treatment group in the use of conventional acupuncture point selection to increase Dachangshu deep acupuncture in the control group only conventional acupuncture points,and the remaining two sets of the same treatment.Treatment after two courses of treatment efficacy evaluation;Mcgill scale used for the two groups were compared in patients with pain assessment;follow-up of 1 year recurrence rate were compared between two groups.Results:The total effective rate 95.6%,control group 83.8% of the total effective rate,differences in the two groups with significant(χ2=6.852,P0.05);of the two groups in the Mcgill Pain Assessment Scale in the index(the pain rating index,PRI) at the feeling,PRI emotional points,PRI score,visual analogue scale(visual analohuc scales,VAS),pain now(the present pain intensity,PPI) 5 comparison areas,the difference with a significant(respectively t= 2.871,3.15,4.40,4.92,3.06,P all 0.01);follow-up of 1 year recurrence rate of 8.9% treatment group,21.7% of the control group,differences between the two groups with significant(P0.05).Conclusion:The deep acupuncture in the Dachangshu based treatment of lumbar disc herniation and the analgesic effect was significantly better than conventional point selection,and low recurrence rate.
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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 | 0.000 |
| 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".