Sodium valproate for painful diabetic neuropathy: a randomized double-blind placebo-controlled study
Bibliographic record
Abstract
BACKGROUND: Various drugs are effective in the management of painful diabetic neuropathy, but none is completely satisfactory. We previously found sodium valproate to be effective and safe in a short-term study. AIM: To test the effectiveness and safety of sodium valproate in the management of painful diabetic neuropathy over 3 months. DESIGN: Randomized double-blind placebo-controlled study. METHODS: Consecutive attending patients with type 2 diabetes mellitus with painful neuropathy were asked to participate in the trial: 48 agreed. Five were excluded: three with HbA(1c) > 11, one with too low a pain level and one who withdrew consent. The remaining 43 were given either drug (group A) or placebo (group B). Each patient was assessed clinically. Quantitative assessment of pain was done by McGill Pain Questionnaire, Visual Analogue Score and Present Pain Intensity, at the beginning of the study, after 1 month and after 3 months. Motor and sensory nerve conduction velocities were measured initially and after 3 months. Liver function tests and other adverse drug-related effects were assessed periodically. RESULTS: Of the 43 patients, four dropped out: one in group A and three in group B. There was significant improvement in pain score in group A, compared to group B, at 3 months (p < 0.001). Changes in electrophysiological data were not significant. The drug was well-tolerated by all patients, except one, who had raised serum AST and ALT levels after 1 month of treatment, and whose treatment was discontinued. DISCUSSION: Sodium valproate is well-tolerated, and provides significant subjective improvement in painful diabetic neuropathy.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".