Deep brain stimulation with short versus conventional pulse width in Parkinson's disease and essential tremor: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: To maximize clinical benefit and minimize stimulation-induced side effects, optimising deep brain stimulation (DBS) parameters is paramount. Recent literature suggests a potential benefit of short pulse width DBS (spDBS; ≤40 μs) over conventional pulse width DBS (cDBS; ≥60 μs) in movement disorders. OBJECTIVE: To compare therapeutic window (TW), therapeutic and side effects and energy consumption of spDBS and cDBS in movement disorders. METHODS: We systematically searched Medline, Embase, Cochrane Library and Web of Science. Appropriate paired analyses were performed. RESULTS: Nine Parkinson's disease (PD) (143 patients), 4 essential tremor (ET) (26 patients) and no dystonia studies were included in the meta-analysis. TW defined as therapeutic amplitude range was larger with spDBS vs. cDBS in PD (standardized mean difference (SMD) = -1.04, p < 0.001) and ET (SMD = -0.71, p < 0.001), but the TW in terms of charge per pulse (CPP) did not differ. In PD, no differences were found in therapeutic and side effects (MDS-UPDRS-III, speech and gait, dyskinesia, non-motor symptoms and quality of life). In ET, Fahn-Tolosa-Marin Tremor Rating Scale was lower with spDBS vs. cDBS (SMD = 0.36, p < 0.001). A qualitative analysis suggested fewer stimulation-induced side effects with spDBS. CPP was lower with spDBS vs. cDBS in PD (SMD = 0.79, p < 0.001) and ET (MD = 46.46 nC, p < 0.001), but real-world data on battery longevity are lacking. CONCLUSION: Although spDBS enlarges the TW as a wider amplitude range in both PD and ET, it does not alter TW defined by CPP. The therapeutic efficacy of spDBS is not different from cDBS in PD, but spDBS apparently induces more tremor reduction in ET.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".