P.012 The new-onset refractory status epilepticus (NORSE/FIRES) family registry
Bibliographic record
Abstract
Background: New-onset refractory status epilepticus (NORSE) is a rare clinical presentation affecting previously healthy individuals. Febrile infection-related epilepsy syndrome (FIRES) is a subcategory of NORSE and applies when a preceding fever occurs. The NORSE/FIRES Family Registry has been developed to gain insight into risk factors and to assess the spectrum of clinical outcomes amongst survivors. Methods: Survivors, surrogates, and physicians can enter patient data into the REDCap-based registry: https://www.norseinstitute.org/norse-registry-2. Information collected includes medical history, clinical presentation, and quality of life, among others. Participants are invited to complete follow-up surveys for up to two years following presentation of seizures. Enrollment is ongoing in multiple languages. Results: 56 participants are enrolled from 12 countries (2-78 years, median: 12.5, IQR: 20.5, 31 survivors). At ≥6 months after onset, survivors experience a mean of ≥12 seizures per month and remain on a median of 4 (IQR: 3) anti-seizure medications. The median quality of life amongst all survivors was rated 4/10 (IQR: 3.5). Conclusions: Preliminary data suggests that survivors of NORSE/FIRES have a high seizure burden and poor quality of life. This international multi-lingual family registry will help develop hypotheses for future studies and provides an opportunity for families to contribute to the scientific understanding of this disease.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.012 |
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".