Motor cortex electrical stimulation applied to patients with complex regional pain syndrome
Bibliographic record
Abstract
Motor cortex stimulation (MCS) is useful to treat patients with neuropathic pain syndromes, unresponsive to medical treatment. Complex regional pain syndrome (CRPS) is a segmentary disease treated successfully by spinal cord stimulation (SCS). However, CRPS often affects large body segments difficult to cover by SCS. This study analyzed the MCS efficacy in patients with CRPS affecting them. Five patients with CRPS of different etiologies underwent a small craniotomy for unilateral 20-grid-contact implantation on MC, guided by craniometric landmarks. Neurophysiological and clinical tests were performed to identify the contacts position and the best analgesic responses to MCS. The grid was replaced by a definitive 4-contacts-electrode connected to an internalized system. Pain was evaluated by international scales. Changes in sympathetic symptoms, including temperature, perspiration, color and swelling were evaluated. Pre-operative and post-operative monthly evaluations were performed during one year. A double-blind maneuver was introduced assigning two groups. One had stimulators turned OFF from day 30-60 and the other from day 60-90. Four patients showed important decrease in pain, sensory and sympathetic changes during the therapeutic trial, while one patient did not have any improvement and was rejected for implantation. VAS and McGill pain scales diminished significantly (p<0.01) throughout the follow-up, accompanied by disappearance of the sensory (allodynea and hyperalgesia) and sympathetic signs. MCS is effective not only to treat pain, but also improve the sympathetic changes in CPRS. Mechanism of action is actually unclear, but seems to involve sensory input at the level of the spinal cord.
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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 | 0.000 |
| 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 | 0.000 | 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".