Use of Antiepileptic Drugs in the Treatment of Chronic Painful Diabetic Neuropathy
Bibliographic record
Abstract
CONTEXT: Up to 25% of individuals with diabetes develop painful diabetic neuropathy, suffering spontaneous pain, allodynia, hyperalgesia, and other unpleasant symptoms. Decreased physical activity, increased fatigue, and mood and sleep problems may result. EVIDENCE ACQUISITION: A MEDLINE search was conducted, limiting searching to double-blind, randomized, controlled trials (1978 to present) of antiepileptic drugs (carbamazepine, gabapentin, pregabalin, topiramate, and lamotrigine) used in the treatment of chronic neuropathic pain. EVIDENCE SYNTHESIS: The most important aspect of treatment is targeted at modification of the underlying disease. However, approaches to symptomatic pain control are essential and include multiple drug classes. Tricyclic antidepressants, including imipramine, nortriptyline, and amitriptyline, have been the mainstays of treatment, but anticholinergic effects, such as dry mouth, blurring of vision, constipation, orthostatic hypotension, and cardiac arrhythmias, as well as other adverse effects, often limit their use. Other treatments include capsaicin, clonidine, acupuncture, and electrical stimulation, suggesting that there is no single effective treatment. First-generation antiepileptic drugs have been shown to be effective in neuropathic pain. The evidence supporting the use of a new generation of antiepileptic drugs in painful diabetic neuropathy is reviewed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".