Patient considerations in the management of multiple sclerosis: development and clinical utility of oral agents
Bibliographic record
Abstract
Multiple sclerosis (MS) is one of the most frequently occurring disabling neurological disorders among young adults in Canada. It is a chronic inflammatory disorder of the central nervous system (CNS) that is thought to be immune mediated in nature. An estimated 55,000-75,000 Canadians suffer from this debilitating disease. Starting in the mid-1990s, we witnessed the beginning of a new era in the treatment of MS. Treatments finally became available to help modify the course of the disease. Early initiation of treatment soon after diagnosis has become the expectation in many MS clinics, warranting many decisions to be made by the patient with the assistance of their health care team. Currently, there are two categories of disease-modifying therapies (DMTs) available: immunomodulatory and immunosuppressant agents. Although disease-modifying therapies are not a cure and are only moderately effective, they offer a possible slowing of any progression that may occur over time, a decrease in relapse activity, and a decrease in the amount of new lesions developing in the CNS found on magnetic resonance imaging. Not only have these agents been partly effective but up to now they have only been available parenterally, which has many limitations, including a major factor in determining the best outcome for the treatment: adherence. Four new DMTs will likely become available to Canadians over the next five years. Fingolimod, cladribine, teriflunomide, and laquinimod are likely to be marketed as the first oral DMTs in Canada. The US Food and Drug Administration approved fingolimod in September 2010 as a first-line therapy for relapsing forms of MS. Dalfampridine is also available in the US as an agent able to improve walking. Even if these agents present with higher efficacy and a promising safety and tolerability profile, thus possibly demonstrating better adherence, it will be imperative for the health care professionals to focus on monitoring and supporting the patient to ensure reliable reporting of side effects and to improve overall adherence. In the near future, more treatments will become available to the MS population, and choices will become even more complex so that ongoing support, open communication, and education are required to tame any uncertainties about decisions made regarding treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".