Exploring Treatment Approaches in Pediatric MOG Antibody–Associated Disease
Bibliographic record
Abstract
Background and Objectives: Diagnostic criteria for anti-myelin oligodendrocyte glycoprotein (MOG) antibody-associated diseases (MOGADs) were published in 2023, but debate regarding optimal therapeutic strategies for pediatric MOGAD exists. The aim of this study was to evaluate treatment approaches and preferred diagnostic investigations for pediatric MOGAD among neurologists. Methods: ®, as well as through QR codes shared at professional neurologic meetings. The questionnaire included 12 questions evaluating clinical decision making after a first and second neuroinflammatory episode, in a child testing positive for MOG-IgG antibody. Demographic questions were included. Responses were evaluated using descriptive statistics. A comparative analysis was conducted between those who self-identified as neuroimmunologists (NIs) and those who did not. Results: A total of 346 neurologists completed the survey (52.3% of general neurologists, 32.1% of NIs, and 15.6% in other neurology fields). Of all respondents, 90.5% chose to send serum MOG-IgG antibody after the first event (59.7% serum, 36.4% CSF + serum). For acute treatment, 84.1% chose to give a 3-5-day course of high-dose IV steroids. Approaches to steroid tapering varied, with 33.0% choosing a 2-4-week taper, 27.2% choosing a 7-12-week taper, and 21% not offering a steroid taper. 56.6% of non-NIs chose to initiate maintenance therapy after the first episode while only 18.9% of NIs chose to do so. After the second episode, 98.3% of all respondents recommended starting maintenance therapy, with rituximab (RTX) (37.1%) being the most frequently chosen agent, followed by monthly IV immunoglobulin (IVIG) (25.6%) and azathioprine (17.1%). NIs selected monthly IVIG (50%) over RTX (27.3%). The duration of treatment in relapsing cases varied: 42.9% elected to maintain treatment for 2 years or less and 35.3% for more than 2 years, and 21.8% chose to continue treatment indefinitely. Discussion: The survey demonstrated substantial variability in management decisions related to MOGAD among neurologists, reflecting current gaps in knowledge about therapies for MOGAD. Future efforts are needed to improve the uptake of knowledge and ensure that current guidelines are effectively translated into clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".