Utility of depth electrode placement in the neurosurgical management of bottom-of-sulcus lesions: technical note
Bibliographic record
Abstract
OBJECTIVE: Small lesions at the depth of the sulcus, such as with bottom-of-sulcus focal cortical dysplasia, are not visible from the surface of the brain and can therefore be technically challenging to resect. In this technical note, the authors describe their method of using depth electrodes as landmarks for the subsequent resection of these exacting lesions. METHODS: A retrospective review was performed on pediatric patients who had undergone invasive electroencephalography with depth electrodes that were subsequently used as guides for resection in the period between July 2015 and June 2017. RESULTS: Ten patients (3-15 years old) met the criteria for this study. At the same time as invasive subdural grid and/or strip insertion, between 2 and 4 depth electrodes were placed using a hand-held frameless neuronavigation technique. Of the total 28 depth electrodes inserted, all were found within the targeted locations on postoperative imaging. There was 1 patient in whom an asymptomatic subarachnoid hemorrhage was demonstrated on postprocedural imaging. Depth electrodes aided in target identification in all 10 cases. CONCLUSIONS: Depth electrodes placed at the time of invasive intracranial electrode implantation can be used to help localize, target, and resect primary zones of epileptogenesis caused by bottom-of-sulcus lesions.
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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 | 0.004 |
| 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 | 0.001 |
| 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 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".