<p>Combining Transcranial Direct Current Stimulation and Transcutaneous Electrical Nerve Stimulation to Relieve Persistent Pain in a Patient Suffering from Complex Regional Pain Syndrome: A Case Report</p>
Bibliographic record
Abstract
PURPOSE: Complex regional pain syndrome (CRPS) is a rare neuropathic pain condition characterized by sensory, motor and autonomic alterations. Previous investigations have shown that transcranial direct current stimulation (tDCS) and transcutaneous electrical nerve stimulation (TENS) can alleviate pain in various populations, and that a combination of these treatments could provide greater hypoalgesic effects. In the present case report, we describe the effect of tDCS and TENS treatment on pain intensity and unpleasantness in a patient suffering from chronic CRPS. RESULTS: The patient was a 37-year-old woman, suffering from left lower limb CRPS (type I) for more than 5 years. Despite medication (pregabalin, tapentadol, duloxetine), rehabilitation treatments (sensorimotor retraining, graded motor imagery) and spinal cord stimulation (SCS), the participant reported moderate to severe pain. Treatments of tDCS alone (performed with SCS turned off during tDCS application, 1 session/day, for 5 consecutive days) did not significantly decrease pain. Combining tDCS with TENS (SCS temporarily turned off during tDCS, 1 session/day, for 5 consecutive days) slightly reduced pain intensity and unpleasantness. DISCUSSION: Our results suggest that combining tDCS and TENS could be a therapeutic strategy worth investigating further to relieve pain in chronic CRPS patients. Future studies should examine the efficacy of combined tDCS and TENS treatments in CRPS patients, and other chronic pain conditions, with special attention to the cumulative and long-term effects and its effect on function and quality of life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| 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".