Pedunculopontine Nucleus Region Deep Brain Stimulation in Parkinson Disease: Surgical Techniques, Side Effects, and Postoperative Imaging
Bibliographic record
Abstract
The pedunculopontine nucleus (PPN) region has received considerable attention in clinical studies as a target for deep brain stimulation (DBS) in Parkinson disease. These studies have yielded variable results with an overall impression of improvement in falls and freezing in many but not all patients treated. We evaluated the available data on the surgical anatomy and terminology of the PPN region in a companion paper. Here we focus on issues concerning surgical technique, imaging, and early side effects of surgery. The aim of this paper was to gain more insight into the reasoning for choosing specific techniques and to discuss shortcomings of available studies. Our data demonstrate the wide range in almost all fields which were investigated. There are a number of important challenges to be resolved, such as identification of the optimal target, the choice of the surgical approach to optimize electrode placement, the impact on the outcome of specific surgical techniques, the reliability of intraoperative confirmation of the target, and methodological differences in postoperative validation of the electrode position. There is considerable variability both within and across groups, the overall experience with PPN DBS is still limited, and there is a lack of controlled trials. Despite these challenges, the procedure seems to provide benefit to selected patients and appears to be relatively safe. One important limitation in comparing studies from different centers and analyzing outcomes is the great variability in targeting and surgical techniques, as shown in our paper. The challenges we identified will be of relevance when designing future studies to better address several controversial issues. We hope that the data we accumulated may facilitate the development of surgical protocols for PPN DBS.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".