Linear oblique craniectomy: A novel method of minimally invasive subdural grid insertion
Bibliographic record
Abstract
Background: Many countries rely upon subdural grid electroencephalography in the planning of epilepsy surgeries. However, craniotomy for subdural grid implantation is known to result in a variety of complications and requires diligence from the surgical team. We describe a minimally invasive method of subdural grid insertion, termed the linear oblique craniectomy, designed to mitigate complications and increase ease of subdural grid insertion. Objective: To demonstrate feasibility of minimally invasive subdural grid insertion utilizing skull anatomy. Methods: Three fresh frozen and embalmed human cadavers underwent surface landmarking and craniectomy to introduce a 4 × 5 cm2 subdural grid over the Sylvian fissure. Anteroposterior lens-shaped craniectomy measured 5 cm in length with 1 cm maximal width. The dura mater was longitudinally incised, and subdural grids were introduced over the Sylvian fissure. Results: The total area of the craniectomy created by the linear oblique approach consists of only approximately 20% of the total area removed by the traditional approach to access the Sylvian fissure for mesial temporal epilepsy monitoring/preoperative planning. The locations of the grids were evaluated by MRI and computed tomography scans postoperatively to ensure accurate alignment with the Sylvian fissure. Conclusion: In this cadaveric study, we demonstrate the linear oblique craniectomy procedure that provides an alternative approach to subdural grid implantation with significantly decreased invasiveness. This surgical approach has the potential of reducing complication rates of subdural grid insertion for surface monitoring of the brain activity and/or neuromachine interface analysis and is associated with significant reduction of surgical time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".