Transcranial Direct Current Stimulation in Neuropathic Pain
Bibliographic record
Abstract
Neuropathic pain (NP) is one of the most common problems contributing to suffering and disability worldwide. Unfortunately, NP is also largely refractory to treatments, with a large number of patients continuing to report significant pain even when they are receiving recommended medications and physical therapy. Thus, there remains an urgent need for additional effective treatments. In recent years, nonpharmacologic brain stimulation techniques have emerged as potential therapeutic options. Many of these techniques and procedures - such as transcranial magnetic stimulation, spinal cord stimulation, deep brain stimulation, and motor cortical stimulation - have very limited availability, particularly in developing countries. Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation procedure that has shown promise for effectively treating NP, and also has the potential to be widely available. This review describes tDCS and the tDCS procedures and principles that may be helpful for treating NP. The findings indicate that the analgesic benefits of tDCS can occur both during stimulation and beyond the time of stimulation. The mechanisms of cortical modulation by tDCS may involve various activities in neuronal networks such as increasing glutamine and glutamate under the stimulating electrode, effects on the μ-opioid receptor, and restoration of the defective intracortical inhibition. Additional research is needed to determine (1) the factors that may moderate the efficacy of tDCS, (2) the dose (e.g. number and frequency of treatment sessions) that results in the largest benefits and (3) the long-term effects of tDCS treatment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".