P.068 A proposed protocol for treatment of acute necrotizing encephalopathy of childhood at Stollery Children’s Hospital
Bibliographic record
Abstract
Background: Acute Necrotizing Encephalopathy of Childhood (ANEC) is an illness characterized by rapidly progressive encephalopathy, typically associated with a precipitating viral illness such as influenza. It is diagnosed clinically and through neuroimaging showing symmetric multifocal lesions involving the deep grey matter, especially the thalamus. Morbidity and mortality in ANEC are high, so prompt recognition and treatment are key, but treatment protocols vary. We propose a management protocol based on a consensus approach and available evidence. Methods: A rapid literature review was conducted. Studies included were meta-analyses, case series, and expert consensus guidelines. Individual case reports were excluded. We identified interventions which have been used, and selected those with evidence or expert support for use in a protocol. The protocol was reviewed with stakeholders in pediatric neurology and PICU. Results: Reported treatments include high-dose steroids, IV immunoglobulins, tocilizumab, and plasmapheresis. The treatment with strongest evidence is high-dose steroids started within 24 hours of presentation. There is frequently reported use of IV immunoglobulins and plasmapheresis, and growing evidence to support use of tocilizumab (IL-6 blockade) within the first 48 hours. Conclusions: Overall, there is strong expert opinion that treatment should be initiated promptly. We present our centre’s protocol to expedite this treatment.
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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 | 0.078 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.013 |
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".