Case Report: Acute Necrotizing Encephalopathy Following COVID-19 Vaccine
Bibliographic record
Abstract
Objectives: Acute necrotizing encephalopathy (ANE) is a rare neurological disorder arising from a para- or post-infectious "cytokine storm. "It has recently been reported in association with coronavirus disease 2019 (COVID-19) infection. Methods: A 56-year-old male with a diagnosis of ANE 48 h following the first dose of ChAdOx1 nCoV-19 vaccination was investigated. Cytokine analyses on serum and cerebrospinal fluid (CSF) were performed. The patient was treated with high-dose corticosteroids and followed clinically and radiologically. Results: Favorable clinical and radiological outcomes were noted. There was an upregulation in serum levels of CXCL5, CXCL1, Il-8, IL-15, CCL2, TGF-B, and EGF, and up-regulation in CSF levels of CXCL5, IL-2, IL-3, and IL-8. Discussion: As COVID-19 infection has been previously reported as a possible rare cause of ANE, we speculate on an aberrant immune response mechanism that was brought about by the vaccine. To increase our understanding of the pathogenesis of ANE in the context of COVID-19 vaccination and to better define its clinical features and outcomes, clinicians and scientists should continue reporting convincing cases of such entities.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".