Diffusion-weighted and perfusion MRI demonstrates parenchymal changes in complex partial status epilepticus
Bibliographic record
Abstract
Diffusion-weighted MRI (DWI) and perfusion MRI (PI) have been mainly applied in acute stroke, but may provide information in the peri-ictal phase in epilepsy patients. Both transient reductions of brain water diffusion, namely a low apparent diffusion coefficient (ADC), and signs of hyperperfusion have been reported in experimental and human epilepsy case studies. We studied 10 patients with complex partial status epilepticus (CPSE) with serial MRI including DWI and PI. All patients showed regional hyperintensity on DWI, and a reduction of the ADC in (i) the hippocampal formation and the pulvinar region of the thalamus (six out of 10 patients), (ii) the pulvinar and cortical regions (two out of 10), (iii) the hippocampal formation only (one out of 10), and (iv) the hippocampal formation, the pulvinar and the cortex (one out of 10). In all patients a close spatial correlation of focal hyperperfusion with areas of ADC/DWI change was present. In two patients hyperperfusion was confirmed in additional SPECT (single photon emission computed tomography) studies. All patients received follow-up MRI examinations showing partial or complete resolution of diffusion and perfusion abnormalities depending on the length of the follow-up interval. The clinical course, EEG and SPECT results all indicate that MRI detected changes related to prolonged epileptic activity. Combined PI and DWI can visualize haemodynamic and tissue changes after CPSE in the hippocampus, thalamus and affected cortical regions.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".