Burst spinal cord stimulation for pain and motor function in Parkinson's disease: A case series
Bibliographic record
Abstract
INTRODUCTION: Spinal cord stimulation (SCS) is an established strategy for pain reduction used in whole world including Japan to treat chronic intractable pain. Pain is a frequent comorbidity of Parkinson's disease (PD), leading to poorer quality of life. SCS has been reported to effectively reduce pain in PD and may also improve motor function, but most studies have employed the modality of tonic stimulation. As such, the effects of SCS using the newly developed paradigm of burst stimulation in PD remain relatively unexplored. METHODS: This case series reviewed PD patients who underwent SCS using BurstDR stimulation to treat intractable lower back pain (LBP). Pain and motor outcomes were assessed before and at several timepoints after implantation over a 24-week observation period. RESULTS: Pain indices (visual analogue scale [VAS] and short-form McGill Pain Questionnaire 2 [SF-MPQ-2] scores) improved in nearly all patients. Improvements were especially notable in the dimension of affective pain (SF-MPQ-2). Functional motor improvements were evident in the Unified Parkinson's Disease Rating Scale (UPDRS), especially walking-related items, and timed-up-and-go (TUG) test performance, which generally persisted through week 24 of observation. CONCLUSION: Burst SCS improved pain (especially the affective component) in PD patients with LBP, with effects generally lasting for at least 24 weeks. Neither paresthesia nor obvious adverse events were experienced in any case. Motor symptoms as scored of UPDRS Part III had the trends of improvement in lower limb akinesia at week 24 and gait at week 4. These findings suggest that burst SCS may be an effective treatment option for LBP and may be influenced to gait-related motor symptoms in PD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".