Possible Autoantibody-Negative Autoimmune Encephalitis in a Sixty-Four-Year-Old Man Post-Varicella-Zoster Virus Vaccination
Bibliographic record
Abstract
Autoimmune encephalitis (AIE) is a rare condition of brain inflammation that can be the result of multiple etiologies such as neoplasms of the ovaries, teratomas, and, more rarely, post-vaccine administration. Vaccine-associated AIE has been reported and described in the literature with vaccines against yellow fever, tetanus, diphtheria, pertussis and polio, Japanese encephalitis, coronavirus disease 2019 (COVID-19) mRNA, and ChAdOx1 nCoV-19 vaccine. Additionally, there are few reported cases of AIE secondary to varicella-zoster vaccine administration. However, these numbers are found in large population studies and none of the cases have been further described. AIE has a variable presentation with non-specific prodromal symptoms occurring in the early stages with progression to neuropsychiatric and dysautonomia in the late stages. Therefore, this presentation introduces diagnostic difficulty especially in the absence of significant laboratory findings. Here, we discuss all reported and described cases within the literature on vaccine-associated AIE and their respective presentations. We also report and describe a case of possible AIE in a 64-year-old male, 1 week post-varicella-zoster vaccine administration. We further discuss the epidemiology, differential diagnosis, treatment, and prognosis of AIE as well as when to raise clinical suspicion for AIE associated with vaccine administration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".