COVID-19 in Patients With Neuromyelitis Optica Spectrum Disorders and Myelin Oligodendrocyte Glycoprotein Antibody Disease in North America
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: To describe the impact of coronavirus disease 2019 (COVID-19) on people with neuromyelitis optica spectrum disorders (NMOSD) and myelin oligodendrocyte glycoprotein antibody disease (MOGAD). METHODS: tests, or Fisher exact tests for categorical variables. Univariate logistic regression models examined effects of risk factors and COVID-19 clinical severity. RESULTS: As of June 7, 2021, 77 patients with NMOSD and 20 patients with MOGAD were reported in the COViMS Registry. Most patients with NMOSD were laboratory positive for SARS-CoV-2 and taking rituximab at the time of COVID-19 diagnosis. Most patients with NMOSD were not hospitalized (64.9% [95% CI: 53.2%-75.5%]), whereas 15.6% (95% CI: 8.3%-25.6%) were hospitalized only, 9.1% (95% CI: 3.7%-17.8%) were admitted to the ICU and/or ventilated, and 10.4% (95% CI: 4.6%-19.5%) died. In patients with NMOSD, having a comorbidity was the sole factor identified for poorer COVID-19 outcome (OR = 6.0, 95% CI: 1.79-19.98). Most patients with MOGAD were laboratory positive for SARS-CoV-2, and almost half were taking rituximab. Among patients with MOGAD, 75.0% were not hospitalized, and no deaths were recorded; no factors were different between those not hospitalized and those hospitalized, admitted to the ICU, or ventilated. DISCUSSION: Among the reported patients with NMOSD, a high mortality rate was observed, and the presence of comorbid conditions was associated with worse COVID-19 outcome. There were no deaths reported in the patients with MOGAD, although these observations are limited due to small sample size.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".