Myelin oligodendrocyte glycoprotein antibody titers by fixed cell-based assay: positive predictive value and impact of sample collection timing
Bibliographic record
Abstract
Introduction: In January 2023, our laboratory began performing serum myelin oligodendrocyte glycoprotein antibody (anti-MOG) titers by fixed cell-based assay (CBA). As a quality assurance (QA) assessment, we evaluated titer positive predictive value (PPV) as well as impact of sample collection timing on titers. Methods: Among patients who underwent antibody titers to distinguish between low-positive (<1:100) and clear-positive (≥1:100) anti-MOG, records were reviewed to classify results as true-positive (TP) or false-positive (FP) and facilitate PPV calculation. Timing of sample collection relative to administration of immunotherapy and symptom onset was determined for TP results. Results: = 0.009). The difference in PPV between low-positive and clear-positive anti-MOG was significant among adults tested, but not children. Among patients with TP anti-MOG, the proportion who received immunotherapy prior to sample collection was significantly higher and median time from symptom onset to sample collection was significantly longer for low-positive compared to clear-positive results. Conclusion: Overall PPV of anti-MOG testing by fixed CBA was reasonably high. The PPV of low-positive anti-MOG was significantly lower than clear-positive anti-MOG. This was driven by the significantly lower PPV of low-positive anti-MOG in adults, possibly reflecting the lower prevalence of MOG antibody-associated disease among adults tested. Timing of sample collection relative to administration of immunotherapy and symptom onset may substantially impact titers, indicating that testing should ideally be performed prior to immunotherapy and close to time of attack.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".