Acute encephalopathy and encephalitis caused by influenza virus infection
Bibliographic record
Abstract
PURPOSE OF REVIEW: Influenza-associated acute encephalopathy/encephalitis (IAE) is an uncommon but serious complication with high mortality and neurological sequelae. This review discusses recent progress in IAE research for a better understanding of the disease features, populations, outcomes, diagnosis, and pathogenesis. RECENT FINDINGS: In recent years, many IAE cases were reported from many countries, including Japan, Canada, Australia, Austria, The Netherlands, United States, Sweden, and other countries and regions. During the novel influenza A/H1N1 pandemic, many IAE cases with A/H1N1 infection in children were reported, particularly in those hospitalized with influenza infection. Pathogenesis of IAE is not fully understood but may involve viral invasion of the CNS, proinflammatory cytokines, metabolic disorders, or genetic susceptibility. An autosomal dominant viral acute necrotizing encephalopathy (ANE) was recently found to have missense mutations in the gene Ran-binding 2 (RANBP2). Another recurrent ANE case following influenza A infection was also reported in a genetically predisposed family with an RANBP2 mutation. SUMMARY: Although IAE is uncommon, compared with the high incidence of influenza infection, it is severe. However, this complication is not duly recognized by health practitioners. Recent advances highlight the threat of this complication, which will help us to have a better understanding of IAE.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".