Transcutaneous electrical nerve stimulation for upper limb neuropathic pain in Guillain-Barré syndrome: a case report
Bibliographic record
Abstract
Neuropathic pain is a common but often under-recognized and undertreated complication of Guillain-Barré syndrome (GBS), with the potential to significantly hinder rehabilitation. We describe the case of a young man with GBS who developed severe upper limb pain, allodynia, and hyperalgesia that were refractory to intravenous immunoglobulin and pharmacologic treatment. The pain caused significant distress and limited therapy participation. A structured trial of transcutaneous electrical nerve stimulation (TENS) was introduced as part of a multimodal rehabilitation program, alongside pharmacological analgesia and routine physical therapy - initially administered during a six-week inpatient stay, then independently at home for another six weeks. High-frequency TENS was applied over the contralateral left paraspinal region at C6-T1, targeting the right upper limb. By weeks two and six, the patient demonstrated clinically meaningful pain relief (revised Short-form McGill Pain Questionnaire), decreased reliance on analgesics, and improved functional independence (motor Functional Independence Measure). Further gains were observed at 12-week follow-up. TENS was well tolerated with no reported adverse effects. This case highlights the potential role of TENS as a safe, noninvasive adjunct for managing GBS-related neuropathic pain and supporting functional recovery. It may also promote long-term self-management beyond the inpatient setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| 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".