Posttraumatic neuropathic pain following spinal cord injury
Bibliographic record
Abstract
Background: Severe posttraumatic neuropathic chronic pain in paralyzed limbs occurs in 10-30% of patients following spinal cord injury. Effective pain relief is a key prerequisite for successful rehabilitation in these individuals. Aim: To identify different patterns of posttraumatic neuropathic pain and determine which pain features are most responsive to surgical intervention. Materials and Methods: A consecutive series of 45 patients with gunshot-induced spinal cord injuries was analyzed for posttraumatic neuropathic pain, focusing on the nature and distribution of the pain. The standard McGill-Melzack Pain Questionnaire was used to assess pain characteristics, while pain distribution was recorded using body maps drawn by the patients. Two distinct pain qualities were identified: thermal (burning) and mechanical (cutting). Pain distribution was classified as either localized or diffuse. Dorsal Root Entry Zone (DREZ) lesion surgery was performed to achieve pain relief. Results: There were 21 patients (47%) suffering from pain of mechanical nature, 7 patients (15%) experiencing pain of pure thermal nature, and 17 patients (38%) with combined mechanical and thermal pain. There were 38 patients (85%) who reported localized pain, while 7 patients (15%) experienced diffuse pain. Pain relief was achieved in all patients with localized pain, regardless of whether the pain was thermal or mechanical. In contrast, DREZ surgery was ineffective in patients with diffuse pain. Conclusion: Localized posttraumatic neuropathic pain was the most common pattern observed in patients following spinal cord injury and showed the highest responsiveness to DREZ surgery. In contrast, diffuse pain demonstrated poor responsiveness to this surgical intervention.
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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) | 0.000 | 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) | 0.003 | 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".