Clinical observation of modified “Huangqi Guizhi Wuwu Decoction” for diabetic peripheral neuropathic pain
Bibliographic record
Abstract
Objective To discuss the clinical efficacy of modifiedHuangqi Guizhi Wuwu Decoctionin treating diabetic peripheral neuropathic pain( DPNP). Methods Sixty-four patients meet the inclusion criteria of DPNP were randomly divided into treatment group and control group,32 cases in each group. The control group was treated with hypoglycemic,antihypertensive,lipid-lowering and Methycobal tablets,gabapentin and other basic treatment,and treatment group was added with modifiedHuangqi Guizhi Wuwu Decoction,with the course of 8 weeks. The pain and TCM syndromes were detected by the McGill Pain Questionnaire. The clinical efficacies of the two groups were compared. Results The McGill Pain Questionnaire showed that the VAS score was reduced after treatment in both groups,and the reduction in treatment group are much than the control group( P 0. 05). There were significant differences in improvement of TCM syndromes and total clinical efficacy between treatment group and control group( P 0. 01). Conclusion ModifiedHuangqi Guizhi Wuwu Decoctionis effective to improve the symptoms of DPNP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".