New imaging approaches for precision diagnosis and disease staging of MS?
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) has revolutionized the diagnosis and management of people living with multiple sclerosis (MS). However, conventional MRI sequences and measures currently used in clinical practice have limitations in the appropriate diagnosis, prediction of future disability, and monitoring of disease activity in MS. A specific challenge is the accurate and timely diagnosis of progressive subtypes of MS. This article will summarize emerging MRI measures that may be of utility as clinical tools in diagnosis and prediction in MS. Although a wide range of MRI techniques have different strengths and weaknesses, those that will be discussed in this article include the "central vein sign," leptomeningeal inflammation/enhancement, conventional and quantitative spinal cord imaging, susceptibility-weighted imaging, and high-field MRI techniques. There are a number of novel and emerging MRI techniques that hold promise in improving diagnosis, prediction, and disease monitoring in MS.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".