Comparison of Monopolar Review to Fixed Parameter Fractionation in Deep Brain Stimulation
Bibliographic record
Abstract
Background: Technological advancements in deep brain stimulation (DBS) require methodological changes in programming. Fractionalization poses significant practical challenges for the most common approach for assessing DBS efficacy, monopolar review (MR). Objectives: Two DBS programming methods: MR and fixed parameter vertical and horizontal fractionalization (FPF) were compared. Methods: A two-phase process of vertical and horizontal FPF was performed. MR was conducted thereafter. After a short wash-out period, both optimal configurations determined by MR and FPF were tested in a double-blind randomized manner. Results: Seven PD patients were enrolled, providing 11 hemispheres to compare the two conditions. In all subjects, the blinded examiner selected a directional or fractionalization configuration. There was no significant difference in clinical benefits between MR and FPF. FPF was the preferred method for initial programming as selected by subject and clinician. Conclusions: FPF programming is a viable and efficient methodology that may be incorporated into clinical practice.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".