Effective accuracy of stereoelectroencephalography: robotic 3D versus Talairach orthogonal approaches
Bibliographic record
Abstract
OBJECTIVE: Stereoelectroencephalography (SEEG) was first developed in the 1950s by Jean Talairach using 2D angiography and a frame-based, orthogonal approach through a metallic grid. Since then, various other frame-based and frameless techniques have been described. In this study the authors sought to compare the traditional orthogonal Talairach 2D angiographic approach with a frame-based 3D robotic procedure that included 3D angiographic interoperative imaging guidance. MRI was used for both procedures during surgery, but MRI preplanning was done only in the robotic 3D technique. METHODS: All study patients suffered from drug-resistant focal epilepsy and were treated at the same center by the same neurosurgical team. Fifty patients who underwent the 3D robotic procedure were compared to the same number of historical controls who had previously been successfully treated with the Talairach orthogonal procedure. The effectiveness and absolute accuracy, as well as safety, of the two procedures were compared. Moreover, in the 3D robotic group, the reliability of the preoperative MRI to avoid vascular structures was evaluated by studying the rate of trajectory modification following the coregistration of the intraoperative 3D angiographic data onto the preoperative MRI-based trajectory plans. RESULTS: Effective accuracy (96.5% vs 13.7%) and absolute accuracy (1.15 mm vs 4.00 mm) were significantly higher in the 3D robotic group than in the Talairach orthogonal group. Both procedures showed excellent safety results (no major complications). The rate of electrode modification after 3D angiography was 43.8%, and it was highest for frontal and insular locations. CONCLUSIONS: The frame-based, 3D angiographic, robotic procedure described here provided better accuracy for SEEG implantations than the traditional Talairach approach. This study also highlights the potential safety advantage of trajectory planning using intraoperative frame-based 3D angiography over preoperative MRI alone.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".