Quality of life over time after new onset refractory status epilepticus
Bibliographic record
Abstract
OBJECTIVE: This study aims to better characterize the long-term neurological quality of life (QOL) outcomes (using the Neuro-QOL scale) in survivors of new onset refractory status epilepticus (NORSE), including its subtype febrile infection-related epilepsy syndrome (FIRES), and provide guidance for psychological and social support strategies. METHODS: Utilizing data from a multicenter prospective study of NORSE/FIRES led by Yale University, we enrolled patients who completed the validated, patient-reported Neuro-QOL scale at least once at 3-6 months (n = 37), 12 months (n = 29), 24 months (n = 23), or ≥36 months (n = 9) following discharge. The Neuro-QOL scale assesses physical, mental, and social health in patients with neurological disorders. QOL impairment (QOL-I) scores were calculated, with higher scores indicating greater impairment. T-scores enabled comparisons with reference populations. RESULTS: In adults, median QOL-I improved from 44.1% at 3-6 months to 37.6% at 36+ months. Paired analysis showed significant improvement in QOL-I between 3-6 and 24 months (p = .016), with specific improvements in communication, satisfaction with social roles, fatigue, and mobility. Greater improvement was also observed for participation in social roles (5.5-point T-score gain) compared to the reference population, suggesting meaningful change. A gradual improvement in overall QOL-I scores was also observed in pediatric participants, despite a modest sample size (n = 5 with data at 3-6 and 12 months). Measures of fatigue and anxiety persisted in adults, and cognitive difficulties persisted in both adults and children. In adults, longer status epilepticus duration and intensive care unit stay were associated with poorer QOL. Additionally, a higher number of antiseizure medications was associated with more depression, cognitive impairments, and perceived stigma. SIGNIFICANCE: These findings highlight the potential for recovery following an acute episode of NORSE, although many patients continue to face challenges requiring ongoing support, and the clinical meaning of the reported QOL improvement remains unclear. Furthermore, the findings underscore the importance of strategic multidisciplinary support systems in the years following discharge.
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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 | 0.001 |
| 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 | 0.001 |
| 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".