Guizhi-Fuling Pill combined with conventional western medicine therapy for diabetic peripheral neuropathy
Bibliographic record
Abstract
Objective To evaluate the therapeutic effect of Guizhi-Fuling Pill combined with conventional western medicine therapy for diabetic peripheral neuropathy (DPN). Methods A total of 78 patients with DNP were randomly divided into treatment group (40 patients) and control group (38 patients).The patients in the control group were administrated with mecobalamin on the basis of conventional treatment. In addition to the therapy of control group, patients in treatment group were given Guizhi-Fuling Pill. The patients in both groups were treated for 2 weeks.The level of homocysteine (Hcy) and sensory nerve conduction velocity (SNCV) and motor nerve conduction velocity (MNCV) in both groups were measured. Toronto clinical scoring system (TCSS) was used to evaluate the curative effect. Results The total effect rate in the treatment group was significantly higher than that in the control group (95.0% vs. 76.3%; χ2=5.616, P=0.018). After treatment, the MNCV of common peroneal nerve (46.1 ± 6.3 m/s vs. 42.5 ± 5.5 m/s; t=2.734, P<0.01 ), MNCV of median nerve (49.8 ± 5.2 m/s vs. 46.3 ± 5.9 m/s; t=2.607, P<0.05 ), SNCV of common peroneal nerve (38.5 ± 4.6 m/s vs. 35.4 ± 4.3 m/s; t=3.105, P<0.05 ), SNCV of median nerve (45.3 ± 5.2 m/s vs. 42.3 ± 4.8 m/s; t=2.627, P<0.05 ) in the treatment group were significantly increased than those in the control group. The integral of TCSS (5.3 ± 3.1 vs. 7.2 ± 2.9; t=2.823, P<0.01) and the level of Hcy (13.3 ± 3.2 µmol/L vs. 17.1 ± 3.4 µmol/L; t=5.178, P<0.05) were significantly lower in treatment group than those in control group (P<0.05). Conclusions The Guizhi-Fuling Pill combined with conventional western medicine therapy could improve MNCV and SNCV and reduce the level of Hcy and improve the clinical effect. Key words: Gui Zhi Fu Ling Wan; Diabetes mellitus, type 2; Peripheral nervous system diseases; Integrated Chinese traditional and western medicine therapy
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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) | 0.001 | 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) | 0.002 | 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".