P.080 Outcomes in Influenza and RANBP2 mutation associated Acute Necrotizing Encephalopathy of Childhood
Bibliographic record
Abstract
Background: Acute Necrotizing Encephalopathy (ANEC) is a rare neuroinflammatory disorder involving the deep grey matter following viral infection and has been associated with the RANBP2 gene. We aimed to evaluate clinical and imaging features in ANEC patients. Methods: This retrospective chart review of ANEC patients (2012-2020) seen at a tertiary pediatric center included analysis of outcomes including ANE-Severity Score, Expanded Disability Status Scale (EDSS) and the modified Rankin Scale (mRS), semi-quantitative imaging scores (degree of swelling or hemorrhage rated 0 (none)-5 (severe/massive)), and dichotomous outcomes including RANBP2 gene status, influenza status. Results: 20 patients were included (Avg. age at presentation 3.5 yrs IQR=3.56., F:M 2.33:1). 3/20 experienced recurrences. All patients with recurrences were positive for RANBP2 mutations. 10/20 patients were influenza positive. 7/20 were RANBP2 mutation positive. We observed higher likelihood of hemorrhage in influenza-positive compared to negative patients (W=78, p=0.048). Kaplan-Meier survival curve analysis revealed that patients without brainstem lesions were more likely to reach minimal/no disability (EDSS<=2) than patients with brainstem lesions (p=0.035). Conclusions: Hemorrhage is more likely to be seen in children with ANEC who are positive for influenza. RANBP2 status was predictive of relapse but not predictive of overall outcome.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".