EFNS guidelines on the use of neuroimaging in the management of multiple sclerosis
Bibliographic record
Abstract
Magnetic resonance (MR)-based techniques are widely used for the assessment of patients with suspected and definite multiple sclerosis (MS). However, despite the publication of several position papers, which attempted to define the utility of MR techniques in the management of MS, their application in everyday clinical practice is still suboptimal. This is probably related, not only, to the fact that the majority of published guidelines focused on the optimization of MR technology in clinical trials, but also to the continuing development of modern, quantitative MR-based techniques, that have not as yet entered the clinical arena. The present report summarizes the conclusions of the 'EFNS Expert Panel of Neuroimaging of MS' on the application of conventional and non-conventional MR techniques to the clinical management of patients with MS. These guidelines are intended to assist in the use of conventional MRI for the diagnosis and longitudinal monitoring of patients with MS. In addition, they should provide a foundation for the development of more widespread but rational clinical applications of non-conventional MR-based techniques in studies of MS patients.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".