International consensus recommendations for management of new onset refractory status epilepticus (NORSE) including febrile infection‐related epilepsy syndrome (FIRES): Summary and clinical tools
Bibliographic record
Abstract
OBJECTIVE: To develop consensus-based recommendations for the management of adult and pediatric patients with NORSE/FIRES based on best available evidence and expert opinion. METHODS: The Delphi methodology was followed. A facilitator group of 9 experts was established, who defined the scope, users and suggestions for recommendations. Following a review of the current literature, recommendation statements concerning diagnosis, treatment and research directions were generated which were then voted on a scale of 1 (strongly disagree) to 9 (strongly agree) by a panel of 48 experts in the field. Consensus that a statement was appropriate was reached if the median score was greater than or equal to 7, and inappropriate if the median score was less than or equal to 3. RESULTS: Overall, 85 recommendation statements achieved consensus. The recommendations are divided into five sections: 1) disease characteristics, 2) diagnostic testing and sampling, 3) acute treatment, 4) treatment in the post-acute phase, and 5) research, registries and future directions in NORSE/FIRES. These are summarized in this article along with two practical clinical flowsheets: one for diagnosis and evaluation and one for acute treatment. A corresponding evidence-based analysis of all 85 recommendations alongside responses by the Delphi panel is presented in a companion article. SIGNIFICANCE: The recommendations generated by this consensus can be used as a guide for the diagnosis, evaluation, and management of patients with NORSE/FIRES, and for planning of future research.
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 | 0.136 | 0.220 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".