The Evolving Role of the Multiple Sclerosis Nurse
Bibliographic record
Abstract
A greater understanding of the pathogenesis of multiple sclerosis (MS) and the need for treatments with increased efficacy, safety, and tolerability have led to the ongoing development of new treatments. The evolution of treatments for MS is expected to have a dramatic impact on the entire health-care team, especially MS nurses, who build strong collaborative partnerships with their patients. MS nurses help patients better understand their disease and treatment options, facilitate the initiation and management of treatment, and encourage adherence. With new oral therapies entering the market, the potential for increased efficacy, tolerability, adherence, and convenience for patients is evident. However, the resulting change in the treatment paradigm means that the skill set required of an MS nurse will inevitably expand. There will be a growing need for professional training and development to ensure that nurses are familiar with the wider range of treatments and their specific modes of action, dosing schedules, and benefit/risk profiles. In addition, the MS nurse's role will expand to include management of the complex monitoring needs specific to each therapy. This article explores how the role of the MS nurse is evolving with the development of new MS therapies, including novel oral therapies.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".