Extinguishing Febrile Infection-Related Epilepsy Syndrome: Pipe Dream or Reality?
Bibliographic record
Abstract
Febrile infection-related epilepsy syndrome (FIRES) is a rare and devastating epileptic encephalopathy with historically abysmal neurocognitive outcomes, including a high incidence of mortality. It tends to affect children and young adults and is characterized by superrefractory status epilepticus following a recent febrile illness. Growing evidence suggests a heterogeneous etiology resulting in fulminant nonantibody-mediated neuroinflammation. For some children with FIRES, this aberrant neuroinflammation appears secondary to a functional deficiency in the endogenous interleukin-1 receptor antagonist. A precise etiology has not been identified in all FIRES patients, and current treatments are not always successful. Limited treatment evidence exists to guide choice, dosing, and duration of therapies. However, the ketogenic diet and certain targeted immunomodulatory treatments, including anakinra, appear safe and have been associated with relatively excellent clinical outcomes in some FIRES patients. Future prospective multicenter collaborative studies are needed to further delineate the FIRES heterogeneous disease pathophysiology and to determine the safety and efficacy of treatment strategies through a robust measurement of neurocognitive outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".