Age‐associated differences in FIRES: Characterizing prodromal presentation and long‐term outcomes via the web‐based NORSE/FIRES Family Registry
Bibliographic record
Abstract
Febrile infection-related epilepsy syndrome (FIRES) is a rare clinical presentation of refractory status epilepticus following a febrile infection. This study analyzes data from the NORSE/FIRES Family Registry, an international web-based registry available in six languages with data entered by patients, families, and clinicians to explore clinical presentations, survivorship, and long-term outcomes in adult and pediatric FIRES patients. We characterize and examine differences in demographics, prodromal symptoms, seizure frequency, anti-seizure medications (ASMs), quality of life, cognition, mood, and anxiety in adults vs pediatric populations with FIRES. Eighty-six participants were included in the study. Pediatric patients (n = 54) were predominantly male (77.8%) and experienced a significantly higher post-FIRES seizure burden than adult survivors (67.7% ≥12 seizures/month in pediatrics vs 11.8% in adults, p <.001). Adults (n = 32) were more likely to be female (59.4%) and have flu-like prodromal symptoms (90.6%). At ≥6 months post-FIRES, both groups exhibited high ASM use, with the majority (87.5%) taking three or more medications. Pediatric patients reported worse mood and anxiety outcomes compared to adults (p <.005). Self-reported quality of life and cognition were rated as moderate across in adults (5.2/10) and pediatric (4.7/10) patients, although pediatric patients indicated poorer cognition. Our findings highlight the challenges in managing post-FIRES outcomes across different age groups, particularly in pediatric patients who face a higher seizure burden and report worse cognitive 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".