Evolving role of MRI in optimizing the treatment of multiple sclerosis: Canadian Consensus recommendations
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging (MRI) is increasingly important for the early detection of suboptimal responders to disease-modifying therapy for relapsing-remitting multiple sclerosis. Treatment response criteria are becoming more stringent with the use of composite measures, such as no evidence of disease activity (NEDA), which combines clinical and radiological measures, and NEDA-4, which includes the evaluation of brain atrophy. METHODS: The Canadian MRI Working Group of neurologists and radiologists convened to discuss the use of brain and spinal cord imaging in the assessment of relapsing-remitting multiple sclerosis patients during the treatment course. RESULTS: Nine key recommendations were developed based on published sources and expert opinion. Recommendations addressed image acquisition, use of gadolinium, MRI requisitioning by clinicians, and reporting of lesions and brain atrophy by radiologists. Routine MRI follow-ups are recommended beginning at three to six months after treatment initiation, at six to 12 months after the reference scan, and annually thereafter. The interval between scans may be altered according to clinical circumstances. CONCLUSIONS: The Canadian recommendations update the 2006 Consortium of MS Centers Consensus revised guidelines to assist physicians in their management of MS patients and to aid in treatment decision making.
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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 | 0.043 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".