PP009 Topic: AS01–Brain: Neuroimaging/Trauma/Monitoring/Status Epilepticus/CNS Infections/Other: INFLUENZA-ASSOCIATED ACUTE NECROTIZING ENCEPHALOPATHY OF CHILDHOOD: A CASE SERIES
Bibliographic record
Abstract
Aims & Objectives: Acute necrotizing encephalopathy of childhood (ANEC) is a rare central nervous system complication of viral and bacterial infections–classically influenza–characterized by acute alteration of consciousness, characteristic neuroimaging findings, with high morbi-mortality. Recognition is challenging and the evidence for specific therapies is very limited. We aim to describe our cases. Methods: Three Influenza A positive children with possible ANEC were identified through the neurology and PICU records during the 2023 fall/winter. A retrospective chart review was performed. Results: Three children aged 2 - 8 years old presented with decreased level of consciousness following a 1- 5 days of cough, rhinorrhea and fatigue. Two had also seizures; all were Influenza A positive and not vaccinated against influenza. MRI showed ANEC suggestive features on two patients; the third had brainstem and thalamic infarction and diffuse cerebral edema on CT. Immune therapy was given at day one and three, including pulse steroids, intravenous immunoglobulin (IVIG) in all, tocilizumab in one, and plasma exchange in one child. One patient died at day 6 despite early immune therapy; another was transferred for inpatient rehabilitation after 13 days, ambulating with supervision. The third child, who began immune therapy on day three, was discharged home after 35 days not yet ambulating independently. Conclusions: Presenting features of our three ANEC case were non-specific and early neuroimaging was key to a timely diagnosis. The small case series limits our ability to draw conclusions between treatment choices and outcomes, hence the need to develop a consensus approach to manage such children. Keywords: encephalopathy, encephalitis, Influenza, acute necrotizing encephalopathy, Immune therapy
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".