Effect of epilepsy magnetic source imaging on intracranial electrode placement
Bibliographic record
Abstract
OBJECTIVE: Intracranial electroencephalography (ICEEG) with chronically implanted electrodes is a costly invasive diagnostic procedure that remains necessary for a large proportion of patients who undergo evaluation for epilepsy surgery. This study was designed to evaluate whether magnetic source imaging (MSI), a noninvasive test based on magnetoencephalography source localization, can supplement ICEEG by affecting electrode placement to improve sampling of the seizure onset zone(s). METHODS: Of 298 consecutive epilepsy surgery candidates (between 2001 and 2006), 160 patients were prospectively enrolled by insufficient localization from seizure monitoring and magnetic resonance imaging results. Before presenting MSI results, decisions were made whether to proceed with ICEEG, and if so, where to place electrodes such that the hypothetical seizure-onset zone would be sampled. MSI results were then provided with allowance of changes to the original plan. RESULTS: MSI indicated additional electrode coverage in 18 of 77 (23%) ICEEG cases. In 39% (95% confidence interval, 16.4-61.4), seizure-onset ICEEG patterns involved the additional electrodes indicated by MSI. Sixty-two patients underwent surgical resection based on ICEEG recording of seizures. Highly localized MSI was significantly associated with seizure-free outcome (mean, 3.4 years; minimum, >1 year) for the entire surgical population (n = 62). INTERPRETATION: MSI spike localization increases the chance that the seizure-onset zone is sampled when patients undergo ICEEG for presurgical epilepsy evaluations. The clinical impact of this effect, improving diagnostic yield of ICEEG, should be considered in surgery candidates who do not have satisfactory indication of epilepsy localization from seizure semiology, electroencephalogram, and magnetic resonance imaging.
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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 | 0.037 |
| 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) | 0.002 | 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".