COVID-19–Associated Acute Asymmetric Hemorrhagic Necrotizing Encephalopathy: A Case Report
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) has been associated with many neurological complications affecting the central nervous system. Purpose: Our aim was to describe a case of COVID-19 associated with a probable variant of acute necrotizing encephalopathy (ANE). Results: A 60-year-old man who presented with a 3-day history of dyspnea, fever, and cough tested positive for severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2). Five days following his admission, the patient was intubated secondary to respiratory failure. Following his extubation 16 days later, he was found to have a left-sided weakness. Magnetic resonance imaging (MRI) of the brain showed hemorrhagic rim-enhancing lesions involving the right thalamus, left hippocampus, and left parahippocampal gyrus. These lesions showed decreased relative cerebral blood flow on MR perfusion and restricted on diffusion-weighted imaging. These neuroimaging findings were consistent with ANE. The left-sided weakness gradually improved over the subsequent weeks. Conclusions: We concluded that COVID-19 can be associated with ANE, a condition believed to be the result of an immune-mediated process with activation of the innate immune system. Future studies must address whether biological drugs targeting the pro-inflammatory cytokines could prevent the development of this condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".