Consensus-Based Expert Recommendations for Diagnosis and Clinical Management of Vanishing White Matter
Bibliographic record
Abstract
Vanishing white matter (VWM) is a rare disorder, characterized by degeneration of CNS white matter, clinically often exacerbated by stressors such as fever and minor head trauma. VWM is caused by biallelic pathogenic variants in the EIF2B1-5 genes, causing reduced activity of eukaryotic initiation of translation factor 2B, resulting in dysregulation of the integrated stress response (ISR). New scientific insights and increased clinical trials in experimental therapies highlight the need for clinical guidelines to improve and standardize care for patients with VWM worldwide. Standardized care is important for therapy development, as it lessens clinical variability of trial participants at study entry, enabling more sensitive evaluation of treatment outcomes. The aim of this study was to develop expert consensus-based recommendations for diagnosis and management of VWM. A real-time Delphi process with a multidisciplinary expert panel was conducted to formulate consensus-based recommendations. A literature review was performed to determine the strength of available evidence supporting each recommendation. The consensus yielded 43 recommendations on diagnosis, including genetic and MRI criteria, and on clinical management concerning disease progression, acute and long-term care, and preventive strategies. All known pathogenic and likely pathogenic EIF2B1-5 variants were identified from the literature and Amsterdam Leukodystrophy Center laboratory. An overview of these EIF2B1-5 variants was composed to facilitate diagnosis. Clinically used drugs may activate the ISR, posing a risk in VWM, or have no effect on or suppress the ISR, being probably safe in VWM. A second literature search explored the effects of clinically frequently used drugs on the ISR. Drugs were categorized into those likely to activate the ISR, suppress it, and have no likely effects on the ISR. Final judgment was achieved in a consensus meeting of experts. A patient management card was developed with input from clinical experts and patient advocates to provide information on these consensus-based recommendations in lay language and bridge the gap between scientific evidence and expert opinion on one side and the practical needs of clinicians and families on the other side. This study contributes to improving and standardizing VWM care based on scientific and expert insights, while highlighting key areas for future research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".