MOGAD and NMOSD: insights on patients’ radiological and laboratory findings from a single UAE center
Bibliographic record
Abstract
Introduction: Although neuromyelitis optica spectrum disorders (NMOSD) and myelin oligodendrocyte glycoprotein antibody disease (MOGAD) are rare diseases, they pose a significant burden on both society and the healthcare system. This study aims to discuss the demographics and patient characteristics of these diseases in a single center in the United Arab Emirates (UAE). Methods: This is a retrospective, descriptive study that included patients with either NMOSD or MOGAD treated at Rashid Hospital, UAE during the period between January 2019 and January 2024. Patients were selected and categorized according to NMOSD criteria, aquaporin-4 antibodies, and MOG antibodies. Patient demographics, clinical characteristics, and medical history were retrieved from their medical records and descriptively analyzed in the light of patients' serological data. Results: = 5) were AQP4-antibody negative. Seventy-four percent of the NMOSD cohort and 43% of the MOGAD cohort were female. For MOGAD patients, disease onset was at a younger age (median onset age of 25 years) compared to the overall study population (mean onset age of 28.94 years). Long segment transverse myelitis was only detected in NMOSD patients (33.3%), and brainstem syndrome with area postrema syndrome was more common in the MOGAD cohort (29% vs. 4%). The rate of positive response to intravenous methylprednisolone as initial therapy was comparable across both cohorts (74% in case of NMOSD and 71% in case of MOGAD). Conclusion: This study provides valuable insights into the status of NMOSD and MOGAD in the UAE, highlighting the need for larger, prospective studies to further characterize these diseases in the local population, as well as the need for improved understanding of the epidemiology and management of these rare but debilitating conditions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".