Diagnosing viral encephalitis and emerging concepts
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review offers a contemporary clinical approach to the diagnosis of viral encephalitis and discusses recent advances in the field. The neurologic effects of coronaviruses, including COVID-19, as well as management of encephalitis are not covered in this review. RECENT FINDINGS: The diagnostic tools for evaluating patients with viral encephalitis are evolving quickly. Multiplex PCR panels are now in widespread use and allow for rapid pathogen detection and potentially reduce empiric antimicrobial exposure in certain patients, while metagenomic next-generation sequencing holds great promise in diagnosing challenging and rarer causes of viral encephalitis. We also review topical and emerging infections pertinent to neuroinfectious disease practice, including emerging arboviruses, monkeypox virus (mpox), and measles. SUMMARY: Although etiological diagnosis remains challenging in viral encephalitis, recent advances may soon provide the clinician with additional tools. Environmental changes, host factors (such as ubiquitous use of immunosuppression), and societal trends (re-emergence of vaccine preventable diseases) are likely to change the landscape of neurologic infections that are considered and treated in clinical practice.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".