Magnetic source imaging versus intracranial electroencephalogram in epilepsy surgery: A prospective study
Bibliographic record
Abstract
OBJECTIVE: Noninvasive brain imaging tests can potentially supplement or even replace the use of intracranial electroencephalogram (ICEEG), an invasive, costly procedure used in presurgical epilepsy evaluation. This study prospectively examined the agreement between magnetic source imaging (MSI) and ICEEG localization in epilepsy surgery candidates. METHODS: Patients completing video monitoring with scalp EEG who had intractable partial epilepsy based on ictal electro-clinico-anatomical features were screened. Forty-nine enrolled patients (mean age, 27 years; range, 1-61 years) completed MSI and ICEEG studies. Decisions about ICEEG and surgery were made at a consensus conference where MSI could only influence ICEEG coverage by indicating supplemental coverage to that already planned by an original hypothesis. RESULTS: The positive predictive value of MSI for seizure localization was 82 to 90%, depending on whether computed against ICEEG alone or in combination with surgical outcome. The kappa score of agreement for MSI with ICEEG was 0.2744 (p < 0.01) INTERPRETATION: MSI yields localizing information with a high positive predictive value in epilepsy surgery candidates who typically require ICEEG. This finding suggests that enough clinical validity exists for MSI to potentially replace ICEEG for seizure localization.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".