Efficacy of motor cortex stimulation in the treatment of neuropathic pain: a randomized double-blind trial
Bibliographic record
Abstract
OBJECT: In this study the authors used a double-blind protocol to assess the efficacy of motor cortex stimulation (MCS) for treating neuropathic pain. METHODS: Eleven patients with unilateral neuropathic pain (visual analog scale [VAS] score 8-10) of different origins and topography were selected for MCS. A 20-contact grid was implanted through a craniotomy centered over the motor cortex contralateral to the painful area. The motor cortex strip was identified using neuroimages, somatosensory evoked potentials, acute electrical stimulation, and corticocortical evoked potentials. Subacute therapeutic stimulation trials allowed the authors to determine the most efficient pair of contacts to use for long-term MCS. The grid was replaced with a 4-contact electrode connected to an internalized stimulator. Bipolar stimulation at a 40-Hz frequency, 90-micro sec pulse width, amplitude 2-7 V, and 1 hour in "ON" and 4 hours in "OFF" mode was used. Pain was evaluated using the VAS, Bourhis, and McGill pain scales applied each month for 1 year. At Day 60 or 90, the stimulators were turned to OFF mode for 30 days in a randomized, double-blind fashion. The statistical tool used was the Wilcoxon test. RESULTS: Three patients did not report improvement in the subacute trial and were excluded from long-term MCS; the remaining patients underwent long-term stimulation. Significant improvement of pain was induced by MCS (p < 0.01); this persisted during the follow-up period. Turning stimulation to OFF mode increased pain significantly (p < 0.05). Improvement at 1 year was >or= 40% (40-86%) in all cases. CONCLUSIONS: Motor cortex stimulation is an efficient treatment for neuropathic pain, according to an evaluation facilitated by a double-blind maneuver. Subacute stimulation trials are recommended to determine the optimum motor cortex area to be stimulated and to identify nonresponders.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".