Differential efficacy of methylcobalamin and alpha-lipoic acid treatment on symptoms of diabetic peripheral neuropathy
Bibliographic record
Abstract
BACKGROUND: Diabetic hyperglycemia damages peripheral nerves by triggering ischemia, oxidative stress, and inflammation. Alpha-lipoic acid (ALA) and methylcobalamin (MC) are known to improve signs of diabetic peripheral neuropathy (DPN), possibly by enhancing neural and vascular endothelial cell metabolism and antioxidant capacity. We evaluated differences in efficacy following short-term MC or ALA treatment on DPN symptoms to guide clinical drug selection. METHODS: Forty DPN patients were randomly divided into MC and ALA treatment groups (both N.=20) and assessed by the Toronto Clinical Neuropathy Scoring System (TCSS), total symptom score (TSS), visual analog scale (VAS) of positive symptoms, and easy sensory test (EST) for negative symptoms before and after 2 weeks of treatment. Serum malondialdehyde (MDA) and superoxide dismutase (SOD) were also measured. RESULTS: Neuropathy as measured by TCSS, TSS, and VAS scores was significantly reduced by both treatments (P<0.05) but magnitude varied by symptom. The VAS score reductions for burning and pain were significantly greater following ALA (P<0.01), while MC reduced numbness and paresthesia VAS scores to a slightly greater extent than ALA (P>0.05). Numbers of abnormal (low-response) points for pressure and pinprick sensation were reduced by MC but not by ALA, while both treatments induced a significant reduction in vibratory perception threshold (P<0.01). Neither MC nor ALA improved temperature sensation or tendon reflexes (P>0.05). Alpha-lipoic acid, increased SOD and reduced MDA (P<0.05), indicating enhanced antioxidant capacity, while MC had no effect. CONCLUSIONS: Due to differences in efficacy, MC or ALA should be chosen according to the symptoms of individual patients.
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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) | 0.001 | 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 | 0.001 |
| 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".