Toward a Neuroimaging Consensus for the Work-up of Adult Genetic Leukoencephalopathies on Behalf of the White Matter Rounds Network: State of Practice
Bibliographic record
Abstract
BACKGROUND: Adult-onset genetic leukoencephalopathies are frequently misdiagnosed due to their overlap with more common acquired white matter diseases. The White Matter (WM) Rounds Network includes clinicians and scientists from more than 15 centers around the world who meet monthly to discuss undiagnosed white matter diseases. A key barrier to optimizing diagnosis that arose during these monthly discussions was the lack of MRI protocol standardization between institutions. We aimed to: 1) assess the state of practice of current MR protocols for investigating suspected genetic leukoencephalopathies and 2) propose a core standard protocol. METHODS: We used a Delphi method to facilitate group judgements. We submitted a survey for feedback to a panel of neuroimaging experts whose final version was circulated to the entire WM Rounds Network. The results were analyzed, and specific recommendations were put forward during Delphi rounds, for which the stop criterion was 75% agreement. KEY MESSAGE: The MR protocols had an average magnet time of 40 minutes (range 25-60) and included: 3-dimensional (3D) T1 MPRAGE and 3D FLAIR obtained in the sagittal plane, axial T2 and T2-FLAIR, axial DWI/ADC, and axial SWI. Cervical and thoracic spine imaging were also frequently performed. A standardized core imaging protocol inclusive of the above-listed sequences would help harmonize sequence acquisition across institutions and promote cost-effective optimization of the imaging work-up of adult-onset leukoencephalopathies. Four additional recommendations were proposed: 1) contrast-enhanced T1 sequences should be performed for patients with strong clinical and/or radiologic suspicion of adult genetic leukoencephalopathy; 2) cervical and thoracic spine MRI may provide diagnostic value; 3) head CT is of little added value and should not be included routinely; and 4) MR spectroscopy, diffusion tensor imaging, and other advanced imaging techniques are not yet ready to be implemented in the protocol but may be helpful in supporting a working diagnosis.
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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 | 0.001 |
| 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".