Canadian Expert Panel Recommendations for MRI Use in MS Diagnosis and Monitoring
Bibliographic record
Abstract
BACKGROUND: A definitive diagnosis of multiple sclerosis (MS), as distinct from a clinically isolated syndrome, requires one of two conditions: a second clinical attack or particular magnetic resonance imaging (MRI) findings as defined by the McDonald criteria. MRI is also important after a diagnosis is made as a means of monitoring subclinical disease activity. While a standardized protocol for diagnostic and follow-up MRI has been developed by the Consortium of Multiple Sclerosis Centres, acceptance and implementation in Canada have been suboptimal. METHODS: To improve diagnosis, monitoring, and management of a clinically isolated syndrome and MS, a Canadian expert panel created consensus recommendations about the appropriate application of the 2010 McDonald criteria in routine practice, strategies to improve adherence to the standardized Consortium of Multiple Sclerosis Centres MRI protocol, and methods for ensuring effective communication among health care practitioners, in particular referring physicians, neurologists, and radiologists. RESULTS: This article presents eight consensus statements developed by the expert panel, along with the rationale underlying the recommendations and commentaries on how to prioritize resource use within the Canadian healthcare system. CONCLUSIONS: The expert panel calls on neurologists and radiologists in Canada to incorporate the McDonald criteria, the Consortium of Multiple Sclerosis Centres MRI protocol, and other guidance given in this consensus presentation into their practices. By improving communication and general awareness of best practices for MRI use in MS diagnosis and monitoring, we can improve patient care across Canada by providing timely diagnosis, informed management decisions, and better continuity of care.
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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 | 0.122 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".