Defining the Clinicoradiologic Syndrome of SARS-CoV-2 Acute Necrotizing Encephalopathy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We characterize clinical and neuroimaging features of SARS-CoV-2-related acute necrotizing encephalopathy (ANE). METHODS: Systematic review of English language publications in PubMed and reference lists between January 1, 2020, and June 30, 2023, in accordance with PRISMA guidelines. Patients with SARS-CoV-2 infection who fulfilled diagnostic criteria for sporadic and genetic ANE were included. RESULTS: was present in 2 children: one known to have this before SARS-CoV-2 infection, and a patient tested because the SARS-CoV-2 infection was the second encephalopathic illness. Three other children with no prior encephalopathy or family history of encephalopathy were negative for this variant. Fifteen (75%) received immunotherapy (with IV methylprednisolone, immunoglobulins, tocilizumab, or plasma exchange): 6 (40.0%) with monotherapy and 9 (60.0%) had combination therapy. Deaths were in 8 of 17 with data (47.1%): a 2-month-old male infant and 7 adults (87.5%) of median age 56 years (33-70 years), 4 of whom did not receive immunotherapy. DISCUSSION: Children and adults with SARS-CoV-2 ANE have similar clinical features and neuroimaging characteristics. Mortality is high, predominantly in patients not receiving immunotherapy and at the extremes of age.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".