Neural Antibody Testing for Autoimmune Encephalitis: A Canadian Single-Centre Experience
Bibliographic record
Abstract
Neural antibodies have emerged as useful biomarkers in suspected autoimmune encephalitis. We reviewed results of neural antibody testing (anti-N-methyl D-aspartate receptor (NMDAR), leucine-rich glioma-inactivated protein (LGI1), contactin-associated protein-like 2 (CASPR2), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR), γ-aminobutyric acid type B receptor (GABA(B)R), dipeptidyl-peptidase-like protein-6 (DPPX), IgLON family member 5 (IgLON5) and glutamic acid decarboxylase-65 (GAD65)) using cell-based assays (CBAs) and tissue indirect immunofluorescence (TIIF) at our centre. Our findings suggest increased clinical sensitivity of CBA compared to TIIF. However, this may come at some expense to clinical specificity, as evidenced by possible false-positive results when weak serum positivity by CBA was observed for certain antibodies (i.e. anti-NMDAR, CASPR2). In such cases, correlation with serum TIIF, as well as CSF CBA and TIIF, aids in identifying true-positive results.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".