Clinical Study of Dokhwalkigisaengtanggagambang(DGG) and Gamisayuktanggagambang(GSG) for Improving Lumbago and Knee Joint Pain
Bibliographic record
Abstract
Objective : This study was to evaluate the effectiveness of the prescription Dokhwalkigisaengtangagambang(DGG) and Gamisayuktanggagambang(GSG), which has been utilized in the treatment of joint disease, for improving low back and knee joint pain. Methods : In the patients for the clinical studies, control group was 28 cases, experimental group was 41 cases. All subjects had low back pain and knee pain. The experimental group was treated with DGG or GSG, the control group was treated with 17 prescriptions. VAS (Visual Analag Scale), WOMAC (Western Ontario and McMasters Universties Osteoarthritis Index) and ODI (Oswestry Low back pain Disability index) measured before and after the prescription administration. Results : In the difference of VAS score, the experimental group (p <0.001) and the control group (p <0.001) were decreased significantly before and after the administration of prescription, and in the comparisons between the experimental group and the control group, experimental group was decreased significantly compared to the control group(p = 0.008). In the WOMAC score, there was no significant difference between the experimental group and the control group. In the difference of ODI items score, lifting (p = 0.020) and sleeping (p = 0.028) index were decreased significantly before and after the administration of prescription. Conclusion : The results indicated that the prescription DGG and GSG can reduce knee pain and low back pain. This study will be helpful for improving joint disease.
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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".