A Personalized Stereotactic Fixture for Implantation of Depth Electrodes in Stereoelectroencephalography
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Bibliographic record
Abstract
BACKGROUND: The stereoelectroencephalographic (SEEG) implantation procedures still represent a challenge due to the intrinsic complexity of the method and the number of depth electrodes required. OBJECTIVES: We aim at designing and evaluating the accuracy of a custom stereotactic fixture based on the StarFix™ technology (FHC Inc., Bowdoin, ME) that significantly simplifies and optimizes the implantation of depth electrodes used in presurgical evaluation of patients with drug-resistant epilepsy. METHODS: Fiducial markers that also serve as anchors for the fixture are implanted into the patient's skull prior to surgery. A 3D fixture model is designed within the surgical planning software, with the planned trajectories incorporated in its design, aligned with the patient's anatomy. The stereotactic fixture is built using 3D laser sintering technology based on the computer-generated model. Bilateral rectangular grids of guide holes orthogonal to the midsagittal plane and centered on the midcommissural point are incorporated in the fixture design, allowing a wide selection of orthogonal trajectories. Up to two additional grids can be accommodated for targeting structures where oblique trajectories are required. The frame has no adjustable parts, this feature reducing the risk of inaccurate coordinate settings while simultaneously reducing procedure time significantly. RESULTS: We have used the fixture for the implantation of depth electrodes for presurgical evaluation of 4 patients with drug-resistant focal epilepsy, with nearly 2-fold reduction in the duration of the implantation procedure. We have obtained a high accuracy with a submillimetric mean positioning error of 0.68 mm for the anchor bolts placed at the trajectory entry point and 1.64 mm at target. CONCLUSIONS: The custom stereotactic fixture design greatly simplifies the planning procedure and significantly reduces the time in the operating room, while maintaining a high accuracy.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it