Self-adjustment of deep brain stimulation delays optimization in Parkinson’s disease
Bibliographic record
Abstract
BACKGROUND: Parkinson's Disease patients undergo time-consuming programming to refine stimulation parameters after deep brain stimulation surgery. OBJECTIVE: To assess whether the use of the advanced functions of a patient's programmer would facilitate programming of deep brain stimulation. METHODS: Thirty patients were randomly allocated to the use of advanced versus simple mode of the patient programmer in this single-centre, prospective, randomized, controlled study. Primary outcome was the number of days required to optimize the stimulation settings. RESULTS: The number of days required to optimize stimulation was significantly lower in the simple mode (88.5 ± 33.1 vs. 142.1 ± 67.4, p = 0.01). In addition, the advanced mode group had a higher number of side effects (5.4 ± 3.1 vs. 2.6 ± 1.9, p = 0.0055). CONCLUSIONS: The use of the advanced functions of patient programmer delays programming optimization and it is associated with a higher number of side effects. These findings highlight the need for other methods for faster and safer stimulation programming.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".