Motor cortex stimulation for chronic neuropathic pain: results of a double-blind randomized study
Bibliographic record
Abstract
Motor cortex stimulation via surgically implanted electrodes has been used as an off-label treatment for chronic neuropathic pain, but its efficacy has not been fully established. We aimed to objectively study the efficacy of motor cortex stimulation and characterize potential predictors of response. In this randomized, double-blind, sham-controlled, single centre trial, we recruited 18 patients with chronic neuropathic pain who did not adequately respond to conventional treatment and had a numerical pain rating scale (NRS) score ≥6. Patients were initially assigned to receive 3 months of active ('on') or sham ('off') stimulation in a double-blind cross-over phase. This was followed by a 3-month single-blind phase, and 6 months of open-label follow-up. A meaningful response in our trial was defined as a ≥30% or 2-point reduction in NRS scores during active stimulation. Using Bayesian statistics, we found a 41.4% probability of response towards on versus off motor cortex stimulation. The probability of improvement during active stimulation (double-blind, single-blind and open-label phases) compared to baseline was 47.2-68.5%. Thirty nine per cent of the patients were considered long-term responders, 71.4% of whom had facial pain, phantom limb pain or complex regional pain syndrome. In contrast, 72.7% of non-responders had either post-stroke pain or pain associated with brachial plexus avulsion. Thirty-nine per cent of patients had a substantial postoperative analgesic effect after electrode insertion in the absence of stimulation. Individuals with diagnoses associated with a good postoperative outcome or those who developed an insertional effect had a near 100% probability of response to motor cortex stimulation. In summary, we found that ∼40% of patients responded to motor cortex stimulation, particularly those who developed an insertional effect or had specific clinical conditions that seemed to predict an appropriate postoperative response.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 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".