Surgical targeting accuracy analysis of six methods for subthalamic nucleus deep brain stimulation
Bibliographic record
Abstract
A commonly adopted surgical target in deep brain stimulation (DBS) procedures, the subthalamic nucleus (STN) is located deep within the brain and is surrounded by delicate deep-brain structures. Symptoms of Parkinson's disease can be reduced by precisely implanting a multi-electrode stimulator at a specific location within the STN and delivering the appropriate signal to the target. A number of techniques have recently been proposed to facilitate STN DBS surgical targeting and thereby improve the surgical outcome. This paper presents a retrospective study evaluating the target localization accuracy and precision of six approaches in 55 STN DBS procedures. The targeting procedures were performed using a neurosurgical visualization and navigation system, which integrates normalized and standardized anatomical and functional information into the planning environment. In this study, we employed as the "gold standard" the actual surgical target locations determined by an experienced neurosurgeon using both pre-operative image-guided surgical target/trajectory planning and intra-operative electrophysiological exploration and confirmation. The surgical target locations determined using each of the six targeting methods were compared with the "gold standards". The average displacement between the actual surgical targets and those planned with targeting approaches was 3.0 +/- 1.3 mm, 3.0 +/- 1.3 mm, 3.0 +/- 1.0 mm, 2.6 +/- 1.1 mm, 2.5 +/- 0.9 mm, and 1.7 +/- 0.7 mm for approaches based on T2-weighted MRI, a brain atlas, T1 and T2 maps, an electrophysiological database, a collection of final surgical targets from previous patients, and the combination of these functional and anatomical data, respectively. The technique incorporating both anatomical and functional data provides the most reliable and accurate target position for STN 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".