P.033 Detection of Myelin Oligodendrocyte Glycoprotein Immunoglobulin G (MOG-IgG) by live and fixed cell-based assays
Bibliographic record
Abstract
Background: MOG-IgG is associated with non-MS demyelinating disease of the optic nerves, spinal cord and brain. Specificity has been issue so we validated the live and fixed MOG-IgG CBAs against the Oxford Autoimmune Neurology Diagnostic Laboratory (OANG) live CBA as a comparator with high specificity. Methods: At BC Neuroimmunology lab (BCNI), 54 MOG-IgG serum samples previously positive by live-CBA at OANG and BCNI were blindly tested by commercial fixed CBA. All 54 MOG IgG positives came from MOG-IgG positive patients. In addition, 256 samples from healthy people and other neurolgic disease were tested. Results: The live MOG-IgG CBA performed at BCNI was 100% concordant (54/54) with OANG live CBA. In contrast, only 49/54 samples were found seropositive by the commercial fixed CBA. The BCNI live-CBA identified 3/256 control samples as positive while 6/256 controls were positive on the fixed commercial CBA. On this cohort the live CBA is 100% sensitive, 98.8% specific and has PPV of 95%. The commercial fixed MOG test is 91% sensitive, 97.6% specific and has PPV of 87.5%. Conclusions: BCNI live MOG-IgG CBAs are in 100% agreement with MOG-IgG. Three positive results in non-MOGAD associated clinical phenotype require further investigation. These data confirm the superiority of the live MOG CBA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".