P.028 Is there utility in duplicating antibody testing for autoimmune encephalitis? A comparison of results obtained from Mayo and Mitogen Dx
Bibliographic record
Abstract
Background: Autoantibody testing for suspected autoimmune encephalitis (AIE) in Alberta is commonly performed by Mitogen Dx (MDx) using cell-based assays (CBAs) for cell surface antibodies and line immunoassay (LA) for intracellular antibodies without confirmatory tissue immunofluorescence/immunohistochemistry (TIFF/IHC). Duplicate testing is often sent to Mayo Clinic (MC) verify, resulting in increased costs. Methods: Antibody panel results were obtained for all patients who had testing sent to both MC and MDx from adult hospitals in Calgary between 2018 and 2020. Positive antibodies were evaluated to be pathogenic/non-pathogenic by chart review and expert consensus. Results: Thirty-four individuals had antibody panels completed at both labs. Overall agreement (positive/negative panel) was fair (κ = 0.24, p =.08), even after excluding low-titre GAD65 antibodies through MC (n=9, 26.5%). MDx reported more non-pathogenic serum results, including: anti-SOX1 (n=3), anti-NMDAR (n=2) and anti-GABA(B)R (n=1). All pathogenic antibodies (n=3) were positive in both laboratories. Conclusions: No new pathogenic antibodies were identified by sending duplicate testing to MC; however, a larger number of non-pathogenic antibodies were reported by MDx, likely due to lack of confirmatory TIFF/IHC. Antibody testing for AIE should be done in labs performing confirmatory TIFF/IHC on all CBA/LA results to avoid unnecessary investigations and/or treatments.
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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 | 0.013 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".