Constant Current versus Constant Voltage: Clinical Evidence Supporting a Fundamental Difference in the Modalities
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is an effective surgical treatment for movement disorders. Early versions of implantable systems delivered stimulation with constant voltage (CV); however, advances in available and newer platforms have permitted programming in constant current (CC). From a treatment management perspective, there are theoretical advantages of CC stimulation. In this case series, we present clinical evidence supporting the maintenance of current regardless of changes to impedance. MATERIALS AND METHODS: This case series included 3 patients with Parkinson's disease status post-bilateral subthalamic nucleus DBS. Patients in this series self-reported intermittent diplopia with pressure applied to the scalp. Patients were subsequently examined and converted from CV to CC and re-examined. Impedances were checked prior to and after conversion from CV to CC as well as while applying pressure to the scalp that induced the adverse effects. RESULTS: Across patients, we observed that compression of the scalp overlying the connector, while patients were maintained in CV, consistently and objectively induced unilateral adduction of an eye. In addition, during scalp compression, while in CV, impedance was reduced, which would increase current delivery. Converting the patients to CC stimulation without changing other stimulation parameters eliminated diplopia and objective findings of eye deviation with compression of the scalp overlying the hardware despite changes in impedance. CONCLUSIONS: In this case series, we provide clinical support for the principal differences between CV and CC stimulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".