Advances in the interventional management of neuropathic pain
Bibliographic record
Abstract
The management of neuropathic pain, defined as pain as a result of a lesion or disease in the somatosensory nervous system, continues to be researched and explored. As conventional methods demonstrate limited long-term efficacy, there is a significant need to discover therapies that offer both longitudinal and sustained management of this highly prevalent disease, which can be offered through interventional therapies. Tricyclic antidepressants (TCAs), gabapentinoids, lidocaine, serotonin norepinephrine reuptake inhibitors (SNRIs), and capsaicin have been shown to be the most efficacious pharmacologic agents for neuropathic pain relief. With respect to infusion therapies, the use of intravenous (IV) ketamine could be useful for complex regional pain syndrome, fibromyalgia, and traumatic spinal cord injury. Interventional approaches such as lumbar epidurals are a reasonable treatment choice for up to 3 months of pain relief for patients who failed to respond to conservative treatment, with a "B" strength of recommendation and moderate certainty. Neuroablative procedures like pulsed radiofrequency ablation work by delivering electrical field and heat bursts to targeted nerves or tissues without permanently damaging these structures, and have been recently explored for neuropathic pain relief. Alternatively, neuromodulation therapy is now recommended as the fourth line treatment of neuropathic pain after failed pharmacological therapy but prior to low dose opioids. Finally, the intrathecal delivery of various pharmacologic agents, such as quinoxaline-based kappa-opioid receptor agonists, can be utilized for neuropathic pain relief. In this review article, we aim to highlight advances and novel methods of interventional management of neuropathic pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".