Efficacy of combined application of traditional Chinese medicine foot bath and mild moxibustion in the treatment o f limb pain in diabetic peripheral neuropathy patients
Bibliographic record
Abstract
Purpose: To determine the efficacy of the combination of traditional Chinese medicine (TCM) foot bath and mild moxibustion in the treatment of limb pain in patients with diabetic peripheral neuropathy (DPN). Methods: A total of 120 patients with symptoms of DPN-induced pain who were hospitalized at the Department of Endocrinology in the 903rd Hospital of PLA, from January 2020 to June 2021, were included in this study. They were randomly assigned to a study group and a control group, with 60 patients in each group. Patients in both groups received conventional treatments and nursing care, while those in study group were additionally treated with a combination of TCM foot bath and mild moxibustion. Treatment efficacy in the patients was evaluated using Short-Form McGill Pain Questionnaire (SF-MPQ), pain rating index (PRI), visual analogue scale (VAS), TCM symptom score (TCMSS), and Toronto Clinical Scoring System (TCSS). Results: Compared with control group, there were significant improvements in pain-related scores, namely, SF-MPQ, (p ˂ 0.05); PRI, (p < 0.01); VAS, (p < 0.05); as well as overall efficacy (p < 0.05). Similar improvements were also observed with regard to TCMSS (p < 0.001) and TCSS (p < 0.001) in the study group. However, there were no significant changes in Adjusted Diabetes Quality of Life (A-DQOL) score and blood glucose control in both groups. Conclusion: Relative to the conventional treatment for DPN, limb pain and disease score of patients are significantly reduced by the combination of TCM foot bath and mild moxibustion treatments. Further clinical trials would be required prior to application in clinical practice.
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 | 0.001 |
| 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".