Long-Term Follow-Up of Motor Cortex Stimulation for Neuropathic Pain in 23 Patients
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 it