First-Line Use of Higher-Efficacy Disease-Modifying Therapies in Multiple Sclerosis: Canadian Consensus Recommendations
Bibliographic record
Abstract
Multiple sclerosis (MS) is characterized by focal inflammatory activity in the central nervous system and a diffuse, compartmentalized inflammation that is the primary driver of neuroaxonal damage and worsening disability. It is now recognized that higher-efficacy disease-modifying therapies (HE-DMT) are often required to treat the complex neuropathological changes that occur during the disease course and improve long-term outcomes. The optimal use of HE-DMTs in practice was addressed by a Canadian panel of 12 MS experts who used the Delphi method to develop 27 consensus recommendations. The HE-DMTs that were considered were the monoclonal antibodies (natalizumab, ocrelizumab, ofatumumab) and the immune reconstitution agents (alemtuzumab, cladribine). The issues addressed included defining aggressive/severe disease, patient selection of the most appropriate candidates for HE-DMTs, baseline investigations and efficacy monitoring, defining suboptimal treatment response, use of serum neurofilament-light chain in evaluating treatment response, safety monitoring, aging and immunosenescence and when to consider de-escalating or discontinuing treatment. The goals of the consensus recommendations were to provide guidelines to clinicians on their use of HE-DMTs in practice and to improve long-term outcomes in persons with MS.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".