Autoimmune diseases and cancers overlapping with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD): A systematic review
Bibliographic record
Abstract
Background: Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) has various similarities with AQP4-IgG-seropositive Neuromyelitis Optica Spectrum Disorder (AQP4-IgG + NMOSD) in terms of clinical presentations, magnetic resonance imaging (MRI) findings, and response to treatment. But unlike AQP4-IgG + NMOSD, which is known to coexist with various autoimmune diseases and cancers, an association of MOGAD with these conditions is less clear. Methods: We conducted a systematic search in PubMed, Scopus, Web of Science, and Embase based on the preferred reporting items for systematic reviews and meta-analysis (PRISMA). Duplicates were removed using Mendeley 1.19.8 (USA production) and the citations were uploaded into Covidence systematic review platform for screening. Results: The most common autoimmune disease overlapping with MOGAD was anti-N-Methyl-D-Aspartate receptor encephalitis (anti-NMDAR-EN), followed by autoimmune thyroid disorders, and the most common autoantibody was antinuclear antibody (ANA), followed by AQP4-IgG (double-positive MOG-IgG and AQP4-IgG). A few sporadic cases of cancers and MOG-IgG-associated paraneoplastic encephalomyelitis were found. Conclusion: Unlike AQP4-IgG + NMOSD, MOGAD lacks clustering of autoimmune diseases and autoantibodies associated with systemic and organ-specific autoimmunity. Other than anti-NMDAR-EN and perhaps AQP4-IgG + NMOSD, the evidence thus far does not support the need for routine screening of overlapping autoimmunity and neoplasms in patients with MOGAD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".