Effect of ligustrazine on diabetic peripheral neuropathy and its influence on the level of serum CRP
Bibliographic record
Abstract
Objective To investigate the effect of ligustrazine on diabetic peripheral neuropathy(DPN) and its influence on the level of serum C reactive protein(CRP). Methods 76 patients with DPN were selected as the subjects. According to the random number table method, the patients were randomly divided into two groups, 38 cases in each group . The control group was given mecobalamin treatment. The study group was treated with ligustrazine on the basis of the control group. The clinical efficacy of the two groups was observed and compared with the Toronto clinical neurological lesion(TCSS) score and serum CRP level. Results The study group was markedly effective in 20 cases(52.6%), effective in 15 cases(39.5%), ineffective in 3 cases(7.9%). In the control group, the markedly effective in 13 cases (34.2%), effective in 14 cases(36.8%), invalid in 11 cases(28.9%). The clinical efficacy of the study group was significantly better than that of the control group(Z=-6.447, P 0.05). The TCSS scores of the two groups were significantly higher than before treatment(all P<0.05). After treatment, the TCSS score of the study group [(7.24±2.36)points]was significantly lower than(9.08±2.94)points of the control group(t=3.009, P<0.05). The serum CRP levels of the two groups were significantly lower than before treatment(all P<0.05), and the serum CRP level of the study group[(2.82±0.36)mg/L]was significantly lower than that of the control group[(3.48±0.96)mg/L](t=3.968, P<0.05). Conclusion Ligustrazine in the treatment of patients with DPN can significantly improve the degree of neuropathy and reduce the level of serum CRP, so the clinical efficacy is ideal. Key words: Ligustrazine; Diabetic neuropathies; Comparative effectiveness research; C-reactive protein
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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".