A.3 Use of diffusion-weighted imaging to distinguish seizure-related change from limbic encephalitis
Bibliographic record
Abstract
Background: Limbic encephalitis (LE) classically causes medial temporal lobe T2-hyperintensity on magnetic resonance imaging (MRI), but this can also occur with seizure activity. Identifying neuroimaging patterns that can distinguish between LE and seizure activity may help avoid diagnostic confusion in such challenging cases. Methods: Through retrospective review of Mayo Clinic patients who had medial temporal lobe T2-hyperintensity on MRI, we identified non-LE patients with seizure-related medial temporal lobe T2-hyperintensity. Their diffusion-weighted imaging (DWI) was reviewed to look for diffusion restriction patterns potentially unique to seizure activity. Next, a control cohort of LE patients with medial temporal lobe T2-hyperintensity was identified, and their DWI was reviewed to see if these diffusion restriction patterns could help distinguish seizure activity from LE. Results: We identified 10 non-LE patients who had medial temporal lobe T2-hyperintensity due to seizure activity; 9/10 had one of two medial temporal lobe diffusion restriction patterns we uncovered as being potentially unique to seizure activity. In contrast, only 5/57 LE patients had one of these diffusion restriction patterns identified, all of whom had seizures reported. Conclusions: We report two diffusion restriction patterns that may help distinguish seizure activity from LE. Recognition of these diffusion restriction patterns should prompt evaluation for possible seizure activity.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".