The Advancing Role of Neuromodulation for the Management of Chronic Treatment-Refractory Pain
Bibliographic record
Abstract
Neuropathic pain is a common cause of disability and health care utilization. While judicious pharmacotherapy and management of comorbid psychological distress can provide for improved quality of life, some patients with treatment-refractory disease require more invasive therapies. Spinal cord stimulation can provide for improvement in pain and decrease in medication utilization, with level 1 evidence supporting its use across various pain etiologies including persistent postoperative neuropathic pain, complex regional pain syndrome, chronic inoperable limb ischemia, treatment refractory angina, and painful diabetic neuropathy. These procedures can be done with acceptably low morbidity and provide a cost-effective solution for those patients in whom medical therapies have failed. Technological innovation in lead design, implantable pulse generator capability, and stimulation algorithms and parameters may further enhance the success of this therapy. Neuromodulation of distal targets such as dorsal root ganglion may permit greater anatomic specificity of the therapy, whereas subthreshold stimulation with high-frequency or burst energy delivery may eliminate noxious and off-target paresthesiae. Such new technologies should be subject to rigorous evaluation as their mechanisms of action and long-term outcomes remain hitherto undefined.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".