Localizing the seizure onset zone by comparing patient postictal hypoperfusion to healthy controls
Bibliographic record
Abstract
Arterial spin labeling (ASL) MRI can provide seizure onset zone (SOZ) localizing information in up to 80% of patients. Clinical implementation of this technique is limited by the need to obtain two scans per patient: a postictal scan that is subtracted from an interictal scan. We aimed to determine whether it is possible to limit the number of ASL scans to one per patient by comparing patient postictal ASL scans to baseline scans of 100 healthy controls. Eighteen patients aged 20-55 years underwent ASL MRI <90 min after a seizure and during the interictal period. Each postictal cerebral blood flow (CBF) map was statistically compared to average baseline CBF maps from 100 healthy controls (pvcASL; patient postictal CBF vs. control baseline CBF). The pvcASL maps were compared to subtraction ASL maps (sASL; patient baseline CBF minus patient postictal CBF). Postictal CBF reductions from pvcASL and sASL maps were seen in 17 of 18 (94.4%) and 14 of 18 (77.8%) patients, respectively. Maximal postictal hypoperfusion seen in pvcASL and sASL maps was concordant with the SOZ in 10 of 17 (59%) and 12 of 14 (86%) patients, respectively. In seven patients, both pvcASL and sASL maps showed similar results. In two patients, sASL showed no significant hypoperfusion, while pvcASL showed significant hypoperfusion concordant with the SOZ. We conclude that pvcASL is clinically useful and although it may have a lower overall concordance rate than sASL, pvcASL does provide localizing or lateralizing information for specific cases that would be otherwise missed through sASL.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".