P.006 Neural antibody testing for autoimmune encephalitis: A Canadian single-centre experience
Bibliographic record
Abstract
Background: We reviewed our autoimmune encephalitis neural antibody testing using brain tissue indirect immunofluorescence (TIIF) and cell-based assays (CBAs) after one year. Methods: Samples were tested from March 2019–March 2020 by TIIF and CBA for anti-NMDAR, LGI1, CASPR2, AMPAR, GABA(B)R, DPPX, IgLON5 and GAD65. Weakly positive or positive CBA, with or without corresponding TIIF positivity, was reported positive. Clinical questionnaires were submitted for clinical-serological correlation. Patients with a compatible clinical phenotype and no more likely alternative diagnosis were classified as true-positives, while all others were flagged as possible false-positives. Results: Twenty of 373 patients (5.4%) had a positive neural antibody. All anti-LGI1 (N=4), GAD65 (N=4), and GABA(B)R (N=1) were classified as true-positives. In contrast, only 3/6 anti-CASPR2 and 3/5 anti-NMDAR were classified as true-positives. Among true-positives, 2/4 anti-LGI and 3/3 anti-CASPR2 were positive by CBA only. All possible false-positive results exhibited only weak serum staining by CBA, with negative serum TIIF and negative CSF CBA/TIIF (if available). Conclusions: Clinical sensitivity of CBA seems higher than TIIF for neural antibodies studied herein, but may come at some expense to clinical specificity. Among patients with weak serum staining by CBA, correlation with serum TIIF, CSF CBA/TIIF, and clinical presentation is recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.007 | 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".