Long-Term Follow-Up of Motor Cortex Stimulation for Neuropathic Pain in 23 Patients
Bibliographic record
Abstract
BACKGROUND: Motor cortex stimulation (MCS) is being offered to patients suffering from neuropathic pain. Outcome prediction, programming and especially sustaining a long-term treatment effect represent major challenges. We report a retrospective long-term analysis of our patients treated with MCS over a median follow-up of 39.1 months. OBJECTIVES: To investigate the time course of the treatment effect in MCS for neuropathic pain. METHODS: Twenty-three closely followed patients treated with MCS were retrospectively analyzed. Reduction in pain measured on a visual analogue scale (VAS) was defined as the primary outcome parameter. VAS pain level and adverse events were documented at the 1-, 3-, 6-, 12-, 18- and 24-month follow-ups. RESULTS: The mean VAS under best medical treatment was 7.8 (SD 1.2, range 5-9) with escalation to 9.3 (SD 0.9, range 6-10) when the patients' medications were missed or delayed. About half of the patients (47.8%) experienced a satisfactory (>50%) reduction in pain during the first month of treatment. The best treatment results were seen at the 3-month follow-up (mean VAS 4.8, SD 1.9, -37.2% compared to baseline). A decline in the treatment effect was generally observed at the subsequent follow-up assessments. Six patients had their devices explanted during the follow-up period due to loss of treatment effect. CONCLUSIONS: In this study, MCS failed to provide long-term pain control for neuropathic pain. Many aspects of MCS still remain unclear, especially the neural circuits involved and their response to long-term stimulation. Means must be developed to overcome the problems in this promising technique.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".