Sodium valproate in the management of painful neuropathy in type 2 diabetes - a randomized placebo controlled study
Bibliographic record
Abstract
OBJECTIVE: To study the effectiveness and safety aspects of sodium valproate in the management of painful neuropathy in patients of type 2 diabetes mellitus. MATERIAL AND METHODS: A randomized double-blind placebo controlled trial of sodium valproate was done in type 2 diabetic patients to assess its efficacy and safety in the management of painful neuropathy. We screened 60 patients but eight patients could not complete the study; hence, the present study was done on 52 patients. Each patient was assessed by clinical examination, pain score by short form of the McGill pain questionnaire (SF-MPQ) and electrophysiological examination, which included motor and sensory nerve conduction velocity, amplitude and H-reflex initially and at the end of 1 month of treatment. RESULTS: Significant improvement was noticed in the pain score of patients receiving sodium valproate in comparison to patients receiving placebo at the end of 1 month (P < 0.05). The changes in electrophysiological data were not significant. The drug was well tolerated by all patients except one who developed a raised aspartate transaminase (AST)/alanine transaminase (ALT) level after 15 days of treatment. CONCLUSION: Sodium valproate is a well-tolerated drug and provides significant subjective improvement in painful diabetic neuropathy. These data provide a basis for future trials of longer duration in a larger group of 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".