Deep Brain Stimulation in the Management of Neuropathic Pain and Multiple Sclerosis Tremor
Bibliographic record
Abstract
Deep brain stimulation (DBS) of the central gray matter was an important component of the surgical management of chronic, drug-refractory, central neuropathic pain until only a decade ago. However, in the recent past, this technique has been increasingly neglected and has been largely replaced by motor cortex stimulation (MCS). The results of MCS, however, are far from uniform, and the best reports quote a range of 50% to 75% success in providing satisfactory pain relief. In recent years, there has been considerable success in treating various movement disorders, particularly in Parkinson's disease (PD) and dystonia, by chronic high-frequency DBS of nuclear structures in the basal ganglia. This technique has also been shown to be relatively effective in some selected cases of tremulous conditions like essential tremor and posttraumatic tremor. However, when the same techniques have been applied to patients with multiple sclerosis tremor (MST), the results have been mixed. As a result, DBS for MST has often been perceived as an unreliable and inconsistent therapeutic intervention. The authors present their experience with the application of DBS in these two relatively unpopular areas for neuromodulation in the current practice of functional stereotactic neurosurgery. The results demonstrate that with careful patient selection, DBS can offer significant functional benefit in both of these difficult clinical conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".