Neural Antibody Testing in Patients with Suspected Autoimmune Encephalitis
Bibliographic record
Abstract
BACKGROUND: Autoimmunity is an increasingly recognized cause of encephalitis with a similar prevalence to that of infectious etiologies. Over the past decade there has been a rapidly expanding list of antibody biomarker discoveries that have aided in the identification and characterization of autoimmune encephalitis. As the number of antibody biomarkers transitioning from the research setting into clinical laboratories has accelerated, so has the demand and complexity of panel-based testing. Clinical laboratories are increasingly involved in discussions related to test utilization and providing guidance on which testing methodologies provide the best clinical performance. CONTENT: To ensure optimal clinical sensitivity and specificity, comprehensive panel-based reflexive testing based on the predominant neurological phenotypic presentation (e.g., encephalopathy) is ideal in the workup of cases of suspected autoimmune neurological disease. Predictive scores based on the clinical workup can aid in deciding when to order a test. Testing of both CSF and serum is recommended with few exceptions. Appropriate test ordering and interpretation requires an understanding of both testing methodologies and performance of antibody testing in different specimen types. SUMMARY: This review discusses important considerations in the design and selection of neural antibody testing methodologies and panels. Increased collaboration between pathologists, laboratorians, and neurologists will lead to improved utilization of complex autoimmune neurology antibody testing panels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".