Therapy Trial Design in Vanishing White Matter
Bibliographic record
Abstract
Vanishing white matter (VWM) is a leukodystrophy caused by recessive variants in the genes EIF2B1-EIF2B5. It is characterized by chronic neurologic deterioration with superimposed stress-provoked episodes of rapid decline. Disease onset spans from the antenatal period through senescence. Age at onset predicts disease evolution for patients with early onset, whereas disease evolution is unpredictable for later onset; patients with infantile and early childhood onset consistently have severe disease with rapid neurologic decline and often early death, whereas patients with later onset have highly variable disease. VWM is rare, but likely underdiagnosed, particularly in adults. Apart from measures to prevent stressors that could provoke acute deteriorations, only symptomatic care is currently offered. With increased insight into VWM disease mechanisms, opportunities for treatment have emerged. EIF2B1-EIF2B5 encode the 5-subunit eukaryotic initiation factor 2B complex, which is essential for translation of mRNAs into proteins and is a principal regulator of the integrated stress response (ISR). ISR deregulation is central to VWM pathology. Targeting components of the ISR has proven beneficial in mutant VWM mouse models, and several drugs are now in clinical development. However, clinical trials in VWM pose considerable challenges: low numbers of known patients with VWM, unpredictable disease course for patients with onset after early childhood, absence of intermediate biomarkers, and novel first-in-human molecular targets. Given these challenges and considering the critical need to offer therapies, we have formulated recommendations for enhanced diagnosis, drug trial setup, and patient selection, based on our expert evaluation of molecular, laboratory, and clinical data.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".