Burst Spinal Cord Stimulation
Bibliographic record
Abstract
INTRODUCTION: Spinal cord stimulation is commonly used for neuropathic pain modulation. The major side effect is the onset of paresthesia. The authors describe a new stimulation design that suppresses pain as well as, or even better than, the currently used stimulation, but without creating paresthesia. METHODS: A spinal cord electrode (Lamitrode) for neuropathic pain was implanted in 12 patients via laminectomy: 4 at the C2 level and 7 at the T8-T9 level for cervicobrachialgia and lumboischialgia, respectively (1 at T11 at another center). During external stimulation, the patients received the classic tonic stimulation (40 or 50 Hz) and the new burst stimulation (40-Hz burst with 5 spikes at 500 Hz per burst). RESULTS: Pain scores were measured using a visual analog scale and the McGill Short Form preoperatively and during tonic and burst stimulation. Paresthesia was scored as present or not present. Burst stimulation was significantly better for pain suppression, by both the visual analog scale score and the McGill Short Form score. Paresthesia was present in 92% of patients during tonic stimulation, and in only 17% during burst stimulation. Average follow-up was 20.5 months. CONCLUSION: The authors present a new method of spinal cord stimulation using bursts that suppress neuropathic pain without the mandatory paresthesia. Pain suppression seems as good as or potentially better than that achieved with the currently used stimulation. Average follow-up after nearly 2 years (20.5 months) suggests that this stimulation design is stable.
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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) | 0.001 | 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) | 0.008 | 0.002 |
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".