Characterization of Subthalamic Nucleus Boundary and Trajectory Recommendations From a Commercially Available Microelectrode Recording Algorithm During Deep Brain Stimulation Surgery for Parkinson Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Microelectrode recordings (MER) within the subthalamic nucleus (STN) are routinely performed during deep brain stimulation (DBS) surgery for Parkinson disease. Commercially available algorithms have been developed to detect STN boundaries and recommend an optimal DBS lead trajectory based on MER data. We aimed to characterize the variance of a broadly used algorithm's STN border estimates and trajectory recommendations. METHODS: MER data from 37 STN-DBS implants in 21 patients were analyzed offline using a semiautomated algorithm making use of oscillatory activity in MER data (HaGuide, Alpha Omega). Software recommendations were computed using the default STN settings across 3 different 'Site Sizes' and 2 'Waiting Times'. For each of the 6 trials, values for the STN Entrance, STN dorsolateral oscillatory region Exit, STN Exit, STN Length, dorsolateral oscillatory region ratio (%), Stimulation Depth, and trajectory recommendations were analyzed. RESULTS: Even with different input parameters, the algorithm's estimates of STN Exit and STN Entrance within the chosen trajectory had low intrasubject variability and were highly correlated with the depth of the final DBS lead as chosen by the clinical team (STN Exit: r = 0.86 and STN Entrance: r = 0.70; both P < .001). However, the algorithm's trajectory recommendations were more sensitive to input parameters, with the algorithm recommending more than 1 trajectory in 42% of implants. CONCLUSION: Semiautomated identification of STN boundaries by a commonly used algorithm is relatively less sensitive to algorithm input parameters and well-correlated with final STN-DBS lead depth as determined by an expert surgical team. However, algorithm-generated optimal trajectory recommendations are more strongly influenced by input parameters and should be interpreted with more caution during DBS implantation.
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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 | 0.002 | 0.020 |
| 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 | 0.001 | 0.000 |
| 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".