Analysis of MDA, SOD, TAOC, MNCV, SNCV, and TSS scores in patients with diabetes peripheral neuropathy
Bibliographic record
Abstract
Abstract To explore the impact of score in patients with diabetes peripheral neuropathy (DPN) treated with traditional Chinese medicine package (TCMP) plus red light therapy and lipoic acid on malondialdehyde (MDA), erythrocyte superoxide dismutase (SOD), total antioxidant capacity (TAOC), motor nerve conduction velocity (MNCV), sensory nerve conduction velocity (SNCV), and Toronto Clinical Scoring System (TSS). A total of 108 patients with DPN hospitalized in the hospital were chosen and divided into groups with the random number table. In the control group (CG) 54 patients were treated with conventional lipoic acid, and 54 patients in the experimental group (EG) accepted TCMP plus red light on the basis of the CG. The MDA, SOD, TAOC, MNCV, SNCV, and TSS scores before treatment and after treatment were compared between the two groups. Before treatment, there was no statistically significant difference in the levels of oxidation indicators, nerve conduction velocity, and symptom scores between the two groups (P > 0.05). After treatment, the MDA in the EG was lower than that in the CG, with a statistical significance difference (P < 0.05). The SOD and TAOC in the EG were higher than those in the CG, and the difference was statistically significant (P < 0.05). The MNCV and SNCV of median nerve, common peroneal nerve, and tibial nerve in the EG were significantly higher than those in the CG (P < 0.05). The TSS score of the EG was lower than that of the CG, and the difference was statistically significant (P < 0.05). The treatment of patients with DPN with lipoic acid plus TCMP and red light therapy can improve the symptoms and signs of disease, promote the recovery of motor and sensory conduction velocity, and optimize the body oxidation indicators.
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 | 0.001 |
| 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".