The seasonality of <scp>new‐onset</scp> refractory status epilepticus (<scp>NORSE</scp>)
Bibliographic record
Abstract
The etiology of new-onset refractory status epilepticus (NORSE), including its subtype with prior fever known as FIRES (febrile infection-related epilepsy syndrome), remains uncertain. Several arguments suggest that NORSE is a disorder of immunity, likely post-infectious. Consequently, seasonal occurrence might be anticipated. Herein we investigated if seasonality is a notable factor regarding NORSE presentation. We combined four different data sets with a total of 342 cases, all from the northern hemisphere, and 62% adults. The incidence of NORSE cases differed between seasons (p = .0068) and was highest in the summer (32.2%) (p = .0022) and lowest in the spring (19.0%, p = .010). Although both FIRES and non-FIRES cases occurred most commonly during the summer, there was a trend toward FIRES cases being more likely to occur in the winter than non-FIRES cases (OR 1.62, p = .071). The seasonality of NORSE cases differed according to the etiology (p = .024). NORSE cases eventually associated with autoimmune/paraneoplastic encephalitis occurred most frequently in the summer (p = .032) and least frequently in the winter (p = .047), whereas there was no seasonality for cryptogenic cases. This study suggests that NORSE overall and NORSE related to autoimmune/paraneoplastic encephalitis are more common in the summer, but that there is no definite seasonality in cryptogenic cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".