Central neuropathic pain
Bibliographic record
Abstract
Central neuropathic pain arises from a lesion or disease of the central somatosensory nervous system such as brain injury, spinal cord injury, stroke, multiple sclerosis or related neuroinflammatory conditions. The incidence of central neuropathic pain differs based on its underlying cause. Individuals with spinal cord injury are at the highest risk; however, central post-stroke pain is the most prevalent form of central neuropathic pain worldwide. The mechanisms that underlie central neuropathic pain are not fully understood, but the pathophysiology likely involves intricate interactions and maladaptive plasticity within spinal circuits and brain circuits associated with nociception and antinociception coupled with neuronal hyperexcitability. Modulation of neuronal activity, neuron–glia and neuro-immune interactions and targeting pain-related alterations in brain connectivity, represent potential therapeutic approaches. Current evidence-based pharmacological treatments include antidepressants and gabapentinoids as first-line options. Non-pharmacological pain management options include self-management strategies, exercise and neuromodulation. A comprehensive pain history and clinical examination form the foundation of central neuropathic pain classification, identification of potential risk factors and stratification of patients for clinical trials. Advanced neurophysiological and neuroimaging techniques hold promise to improve the understanding of mechanisms that underlie central neuropathic pain and as predictive biomarkers of treatment outcome. Central neuropathic pain (CNP), that is, pain caused by a lesion of disease of the central somatosensory nervous system, severely impacts quality of life. In this Primer, Finnerup and colleagues review the epidemiology, pathophysiology, clinical presentation and diagnosis of CNP as well as discuss common therapeutic strategies and unmet clinical needs in this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".