Acupuncture As A Promising Treatment For Below-Level Central Neuropathic Pain: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Below-level central neuropathic pain, a diffuse pain characterized by generalized burning, is commonly experienced by individuals with spinal cord injury (SCI). The objective of this study was to investigate the effects of an electroacupuncture protocol for the treatment of below-level central neuropathic pain developed at the Toronto Rehabilitation Institute, Lyndhurst Center, Toronto, Ontario, Canada. METHOD: Retrospective chart review. RESULTS: Thirty-six individuals with traumatic and nontraumatic SCI met the inclusion criteria. Of these, 24 showed improvement after treatment with the electroacupuncture protocol. Type of injury, level of injury, and duration of below-level central neuropathic pain was not correlated with improvement. However, individuals whose pain was described as bilateral (vs unilateral; P = 0.014) or symmetric (vs nonsymmetric; P = 0.026) were more likely to improve after acupuncture treatment. Overall, patients whose burning pain was bilateral, symmetric, and constant (P = 0.005) were the most likely to improve. CONCLUSION: This retrospective study suggests that the Lyndhurst Center Central Neuropathic Pain Acupuncture Protocol may be an effective treatment option for patients with SCI who are experiencing below-level central neuropathic pain. Additional prospective clinical studies are needed to confirm these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".