Novel techniques for the diagnosis of neurological infections
Bibliographic record
Abstract
PURPOSE OF REVIEW: On World Encephalitis Day 19th February 2025, Encephalitis International launched the World Health Organization technical brief on encephalitis, highlighting the growing public health challenge and need for improved diagnostics. This review summarizes the published literature over the last 18 months on novel methods of identifying the aetiology of neurological infections and existing research gaps. RECENT FINDINGS: There is an increased availability and sensitivity of multiplex polymerase chain reaction assays and untargeted metagenomic sequencing in clinical practice. This is contributing to increasing diagnostic yield in suspected neurological infections. Preliminary results suggest that novel serological methods such as phage immunoprecipitation sequencing (Phip-seq) may be useful where molecular approaches are negative. SUMMARY: Significant progress in improving diagnostics has been made in the last decade. Going forward, multicentre studies and meta-analyses are needed to achieve adequate power in ascertaining the role of novel diagnostic methods in neurological infections. Studies need to investigate the impact on patient management and cost-effectiveness. The role of other omics methods in identifying host biomarkers for utilization in diagnostic algorithms needs further work.
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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".