Investigation of traditional Chinese medicine foot bath on diabetic peripheral neuropathy
Bibliographic record
Abstract
Objective To investigate the clinical effect of traditional Chinese medicine foot bath on diabetic peripheral neuropathy(DPN).Methods 140 patients with DPN were randomly divided into two groups.Control group received conventional therapy(Methylcobalamin tablets,beraprost sodium tablets and lipoic acid injection).Treatment group received traditional Chinese medicine foot bath on the basis of control group treatment.Score of Toronto clinical scoring system(TCSS),lower extremity nerve function [motor evoked potential latency(LAT),MCV and SCV] and lower extremity vascular index [IMT,PSV,diameter stenosis,resting vascular diameter(D0) and the filling vascular diameter(D1)] were observed after 8 weeks of continuous treatment.The severity of DPN was evaluated by TCSS score.Results There was significant difference between before and after treatment on TCSS score and the severity(without,mild,moderate,and severe) of DPN in two groups(P0.05).There was significant difference between two groups on the levels of LAT,MCV and SCV after treatment(P0.05).There was significant difference between two groups on the levels of IMF,PSV,diameter stenosis and D1-D0/D0 after treatment(P0.05).Conclusion s Traditional Chinese medicine foot bath can obviously improve clinical symptoms of patients with DPN,neurological function,improve blood circulation,promote nerve repair and reduce the severity.
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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".