A Quality Assessment of Aquaporin-4 & Myelin Oligodendrocyte Glycoprotein Antibody Testing
Bibliographic record
Abstract
BACKGROUND: Accurate anti-aquaporin-4 (AQP4) and anti-myelin oligodendrocyte glycoprotein (MOG) autoantibody assays are needed to effectively diagnose neuromyelitis optica spectrum disorder and MOG antibody-associated disease. A proportion of patients at our centre have been tested for anti-AQP4 and anti-MOG autoantibodies locally, followed by an outsourced test as part of real-world practice. Outsourced testing is costly and of unproven utility. We conducted a quality improvement project to determine the value of outsourced testing for anti-AQP4 and anti-MOG autoantibodies. METHODS: All patients seen by Calgary neurological services who underwent cell-based testing for anti-AQP4 and/or anti-MOG autoantibodies at both MitogenDx (Calgary, AB) and Mayo Clinic Laboratories (Rochester, MN, USA) between 2016 and 2020 were identified from a provincial database. The interlaboratory concordance was calculated by pairing within-subject results collected no more than 365 days apart. Retrospective chart review was done for subjects with discordant results to determine features associated with discordance and use of outsourced testing. RESULTS: Fifty-seven anti-AQP4 and 46 anti-MOG test pairs from January 2016 to July 2020 were analyzed. Concordant tests pairs comprised 54/57 (94.7%, 95%CI 88.9-100.0%) anti-AQP4 and 41/46 (89.1%, 95%CI 80.1-98.1%) anti-MOG results. Discordant anti-AQP4 pairs included two local weak positives (negative when outsourced) and one local negative (positive when outsourced). Discordant anti-MOG pairs were all due to local weak positives (negative when outsourced). CONCLUSION: Interlaboratory discordant results for cell-based testing of anti-AQP4 autoantibodies were rare. Local anti-MOG weak positive results were associated with discordance, highlighting the need for cautious interpretation based on the clinical context. Our findings may reduce redundant outsourced testing.
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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 | 0.026 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".