The White Matter Rounds experience: The importance of a multidisciplinary network to accelerate the diagnostic process for adult patients with rare white matter disorders
Bibliographic record
Abstract
Introduction Adult genetic leukoencephalopathies are rare neurological disorders that present unique diagnostic challenges due to their clinical and radiological overlap with more common white matter diseases, notably multiple sclerosis (MS). In this context, a strong collaborative multidisciplinary network is beneficial for shortening the diagnostic odyssey of these patients and preventing misdiagnosis. The White Matter Rounds (WM Rounds) are multidisciplinary international online meetings attended by more than 30 physicians and scientists from 15 participating sites that gather every month to discuss patients with atypical white matter disorders. We aim to present the experience of the WM Rounds Network and demonstrate the value of collaborative multidisciplinary international case discussion meetings in differentiating and preventing misdiagnoses between genetic white matter diseases and atypical MS. Methods We retrospectively reviewed the demographic, clinical and radiological data of all the subjects presented at the WM Rounds since their creation in 2013. Results Seventy-four patients (mean age 44.3) have been referred and discussed at the WM Rounds since 2013. Twenty-five (33.8%) of these patients were referred by an MS specialist for having an atypical presentation of MS, while in most of the remaining cases, the referring physician was a geneticist (23; 31.1%). Based on the WM Rounds recommendations, a definite diagnosis was made in 36/69 (52.2%) patients for which information was available for retrospective review. Of these diagnosed patients, 20 (55.6%) had a genetic disease, 8 (22.2%) had MS, 3 (8.3%) had both MS and a genetic disorder and 5 (13.9%) had other non-genetic conditions. Interestingly, among the patients initially referred by an MS specialist, 7/25 were definitively diagnosed with MS, 5/25 had a genetic condition (e.g., X-linked adrenoleukodystrophy and hereditary small vessel diseases like Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) and COL4A1 -related disorder), and one had both MS and a genetic demyelinating neuropathy. Thanks to the WM Rounds collaborative efforts, the subjects who currently remain without a definite diagnosis, despite extensive investigations performed in the clinical setting, have been recruited in research studies aimed at identifying novel forms of genetic MS mimickers. Conclusions The experience of the WM Rounds Network demonstrates the benefit of collective discussions on complex cases to increase the diagnostic rate and decrease misdiagnosis in patients with rare or atypical white matter diseases. Networks of this nature allow physicians and scientists to compare and share information on challenging cases from across the world, provide a basis for future multicenter research studies, and serve as model for other rare diseases.
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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".