Efficacy and safety of motor cortex stimulation for chronic neuropathic pain: critical review of the literature
Bibliographic record
Abstract
OBJECT: The authors systematically reviewed the published literature to evaluate the efficacy of and adverse effects after motor cortex stimulation (MCS) for chronic neuropathic pain. METHODS: A search of the PubMed database (1991-2006) using the key words "motor cortex," "stimulation," and "pain" yielded 244 articles. Only original nonduplicated articles were selected for further analysis; 14 studies were identified for critical review. All were series of cases and none was controlled. The outcomes in 210 patients were assessed and expressed as the percentage of patients that improved with the procedure. Results A good response to MCS (pain relief > or = 40-50%) was observed in approximately 55% of patients who underwent surgery and in 45% of the 152 patients with a postoperative follow-up > or = 1 year. Visual analog scale scores were provided in 76 patients, revealing an average 57% improvement in the 41 responders. A good response was achieved in 54% of the 117 patients with central pain and 68% of the 44 patients with trigeminal neuropathic pain. Adverse effects were reported in 10 studies, including 157 patients. Infections (5.7%) and hardware-related problems (5.1%) were relatively common complications. Seizures occurred in 19 patients (12%) in the early postoperative period, but no chronic epilepsy was reported. Conclusions The results of the authors' review of the literature suggest that MCS is safe and effective in the treatment of chronic neuropathic pain. Results must be considered with caution, however, as none of the trials were blinded or controlled. Studies with a better design are mandatory to confirm the efficacy of MCS for chronic 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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".