Therapeutic effects of gabapentin combined with alpha lipoic acid on painful diabetic peripheral neuropathy
Bibliographic record
Abstract
Objective To observe the efficacy and safety of gabapentin combined with alpha 13 lipoic acid in the treatment of painful diabetic peripheral neuropathy(PDPN).Methods One hundred and thirty-six PDPN patients according to the random number table,were divided into control group(46 cases) and alpha lipoic acid group 44 cases,gabapentin combined with alpha lipoic acid group(combined group) 46 cases,4 weeks of treatment,observed the curative effect of the three groups,before and after the treatment,the visual simulation(VAS) scores,Toronto clinical scoring system(TCSs)score,motor nerve conduction velocity(MNCV),feeling of nerve conduction velocity(SNCV) changes and each treatment process' adverse reactions were observed.Results After the treatment,combined group's total effective rate(91.3%) was higher than that of the alpha lipoic acid group(68.2%) and control group(60.9%)(χ~2= 11.71,χ~2= 7.52,P 0.05).The 3 groups after treatment's VAS,TCSS scores were decreased(P 0.05),compared with control group and alpha lipoic acid group,combined group decreased more significantly(VAS,q =17.15,q =9.78,TCSS,q =11.12,q =6.58,P 0.05),and alpha lipoic acid group's VAS,TCSS had significant difference from control group(q =7.18,q =4.42,P 0.05);3 groups' nerve conduction velocity was increased after treatment(P 0.05),significant increase of SNCV in combined group was found than control group and alpha lipoic acid group(the median nerve,q =6.76,2.34;common peroneal nerve,q = 0.89,q = 4.26,P 0.05),and alpha lipoic acid group's MNCV,SNCV increased more obviously than the control group,the difference was statistically significant(P 0.05).No adverse reactions were observed in the 3 groups,which needed clinical intervention or discontinuation of treatment.Conclusion Gabapentin combined with alpha lipoic acid can effectively relieve the clinical symptoms of patients with PDPN,and the combined therapy is better than single drug,also has a certain degree of security.
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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".