Treatment of MOG antibody associated disorders: results of an international survey.
Bibliographic record
Abstract
Introduction: While monophasic and relapsing forms of myelin oligodendrocyte glycoprotein antibody associated disorders (MOGAD) are increasingly diagnosed world-wide, consensus on management is yet to be developed. Objective: To survey the current global clinical practice of clinicians treating MOGAD. Method: Neurologists worldwide with expertise in treating MOGAD participated in an online survey (February-April 2019). Results: Fifty-two responses were received (response rate 60.5%) from 86 invited experts, comprising adult (78.8%, 41/52) and paediatric (21.2%, 11/52) neurologists in 22 countries. All treat acute attacks with high dose corticosteroids. If recovery is incomplete, 71.2% (37/52) proceed next to plasma exchange (PE). 45.5% (5/11) of paediatric neurologists use IV immunoglobulin (IVIg) in preference to PE. Following an acute attack, 55.8% (29/52) of respondents typically continue corticosteroids for ≥ 3 months; though less commonly when treating children. After an index event, 60% (31/51) usually start steroid-sparing maintenance therapy (MT); after ≥ 2 attacks 92.3% (48/52) would start MT. Repeat MOG antibody status is used by 52.9% (27/51) to help decide on MT initiation. Commonly used first line MTs in adults are azathioprine (30.8%, 16/52), mycophenolate mofetil (25.0%, 13/52) and rituximab (17.3%, 9/52). In children, IVIg is the preferred first line MT (54.5%; 6/11). Treatment response is monitored by MRI (53.8%; 28/52), optical coherence tomography (23.1%; 12/52) and MOG antibody titres (36.5%; 19/52). Regardless of monitoring results, 25.0% (13/52) would not stop MT. Conclusion: Current treatment of MOGAD is highly variable, indicating a need for consensus-based treatment guidelines, while awaiting definitive clinical trials.
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 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".