Child Neurology (Epilepsy and EEG)
Bibliographic record
Abstract
Background: Febrile infection-related epilepsy syndrome (FIRES) is a devastating entity characterized by acute onset of refractory epileptic status preceded by a febrile infection, for which no aetiology has been identified so far. Methods: We report the cases of two males presenting with typical FIRES, for whom extensive investigations revealed no specific aetiology. Failure of controlling seizures with multiple anticonvulsants as well as barbiturate coma lead to the decision to try immunotherapy. The first patient received plasma exchange after a negative trial of IVIGs, while the second received plasma exchange in the beginning. Results: Significant improvement in the seizure frequency and intensity was obtained following plasma exchange, and weaning of barbiturate coma was successful in the days following treatment. Both patients remain with significant temporal lobe epilepsy, requiring treatment with 3 anti epileptics. However, cognitive outcome is surprisingly good for both, both exhibiting normal IQs and normal everyday function, with the second patient showing even better recovery, possibly due to earlier treatment with plasma exchange. Conclusion: Our findings of favourable outcome with plasma exchange favours the auto-immune hypothesis often discussed in FIRES. While awaiting further insight onto the aetiology of this syndrome, we suggest a trial of plasma exchange in patients affected.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".