New onset refractory status epilepticus: Long‐term outcomes beyond seizures
Bibliographic record
Abstract
We propose and prioritize important outcome domains that should be considered for future research investigating long-term outcomes (LTO) after new onset refractory status epilepticus (NORSE). The study was led by the international NORSE Institute LTO Working Group. First, literature describing the LTO of NORSE survivors was identified using a PubMed search and summarized to identify knowledge gaps. Subsequently, a consensus-building process was performed to prioritize and rank important LTO domains for further research. The prioritization of LTO domains was qualitative, enabling the expert panel to generate ideas, share opinions, and provide reasons for the rankings. A second round took place to allow expansion and agreement regarding specific details for each domain. Outcomes were classified into eight main domains: (1) Function: Neuropsychological, Neurological (other than seizures), and Psychiatric (mood and behavior); (2) Quality of Life; (3) Epilepsy; (4) Nonneurological (medical); (5) Social; (6) Caregiver Burden; (7) Long-Term Mortality; and (8) Health Care System Impact. In addition, the working group suggested obtaining outcome measures for each domain at 6 months and 1 year after discharge and annually thereafter until stability has been reached. There are no currently established time frames set for when LTO in NORSE begin or plateau, and previously there existed no consensus regarding which LTO should be considered. This consensus process identifies and recommends NORSE LTO domains that should be considered in future research studies to provide more consistent results that can be compared between studies. Survivors of NORSE should be evaluated serially and at fixed points over time to maximize our understanding of the recovery trajectory for all LTO domains. Establishing reliable and standardized data describing LTO (beyond seizures) after NORSE will support discussions with families during the acute stages, prognostication, the development of targeted management strategies for survivors, and future comparative research globally helping to identify biomarkers that may predict LTO.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".