Epilepsy‐predictive magnetic resonance imaging changes following experimental febrile status epilepticus: Are they translatable to the clinic?
Bibliographic record
Abstract
Summary Objective A subset of children with febrile status epilepticus (FSE) are at risk for development of temporal lobe epilepsy later in life. We sought a noninvasive predictive marker of those at risk that can be identified soon after FSE, within a clinically realistic timeframe. Methods Longitudinal T2‐weighted magnetic resonance imaging (T2WI MRI) of rat pups at several time points after experimental FSE (eFSE) was performed on a high‐field scanner followed by long‐term continuous electroencephalography. In parallel, T2WI MRI scans were performed on a 3.0‐T clinical scanner. Finally, chronic T2WI MRI signal changes were examined in rats that experienced eFSE and were imaged months later in adulthood. Results Epilepsy‐predicting T2 changes, previously observed at 2 hours after eFSE, persisted for at least 6 hours, enabling translation to the clinic. Repeated scans, creating MRI trajectories of T2 relaxation times following eFSE, provided improved prediction of epileptogenesis compared with a single MRI scan. Predictive signal changes centered on limbic structures, such as the basolateral and medial amygdala. T2WI MRI changes, originally described on high‐field scanners, can also be measured on clinical MRI scanners. Chronically elevated T2 relaxation times in hippocampus were observed months after eFSE in rats, as noted for post‐FSE changes in children. Significance Early T2WI MRI changes after eFSE provide a strong predictive measure of epileptogenesis following eFSE, on both high‐field and clinical MRI scanners. Importantly, the extension of the acute signal changes to at least 6 hours after the FSE enables its inclusion in clinical studies. Chronic elevations of T2 relaxation times within the hippocampal formation and related structures are common to human and rodent FSE, suggesting that similar processes are involved across species.
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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 | 0.002 |
| 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 | 0.001 |
| 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".