Clinical effect analysis of Xiaoke-Yuzu decoction on diabetic peripheral neuropathy
Bibliographic record
Abstract
Objective To observe the clinical effect of Xiaoke-Yuzu decoction on diabetic peripheral neuropathy (DPN). Methods A total of 100 DPN inpatients were recruited and randomly divided into the treatment and control groups. The two groups were both received basic therapy, while the treatment group additionally received Xiaoke-Yuzu decoction. Toronto clinical scores and Chinese medicine symptom scores of both groups were collected to evaluate the clinical effect before and after the therapy. Results The Toronto scores of treatment group were significantly lower than control group after treatment (symptoms score 1.50 ± 0.94 vs. 2.23 ± 1.01, reflection score 3.60 ± 1.77 vs. 4.27 ± 1.72, feeling test score 1.53 ± 0.63 vs. 2.10 ± 0.84, all P<0.05). Meanwhile, the Chinese medicine symptom scores of treatment group were also significantly lower than the control group (main symptom score 1.77 ± 1.17 vs. 3.17 ± 1.82, posterior symptom score 2.23 ± 1.59 vs. 4.27 ± 1.57, the tongue and pulse score 1.83 ± 0.65 vs. 2.47 ± 0.51, all P<0.05). Conclusion Xiaoke-Yuzu decoction plus basic therpy could improve the clinical symptoms of DPN patients. Key words: Xiaoke-Yuzu decoction; Diabetic peripheral neuropathy; Clinical effect
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) | 0.001 | 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".