The Dilemma of When to Stop Disease-Modifying Therapy in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Disease-modifying therapy (DMT) has changed the landscape of multiple sclerosis (MS) care. However, there is lack of consensus on the duration of treatment and the selection of individuals most likely to benefit from continued treatment. Current evidence, practice guidelines, health policy, and ethical considerations presented together may further inform challenging clinical decision making and future directions. The objectives of this study were to conduct a narrative review of original research and practice guideline recommendations on discontinuation of DMTs in MS; to collect information regarding Canadian regional reimbursement policies for DMT coverage in MS; and to present ethical considerations applicable to such decision making. METHODS: A literature review was conducted of the MEDLINE/PubMed, OneFile (GALE), Scopus (Elsevier), and ProQuest Biological Science Collection databases. Data regarding Canadian regional reimbursement policies for DMT coverage in MS were collected from the ministry/government websites. Ethical considerations were reviewed in the context of the identified evidence, guidelines, and policies. RESULTS: The literature lacks evidence from prospective randomized controlled trials that directly addresses the issue of discontinuation of DMTs in MS. Current practice guidelines advocate the vital role of patient choice in decision making. There are regional variations in Expanded Disability Status Scale criteria scores for continuing MS DMT coverage among Canadian provinces/territories. CONCLUSIONS: In the absence of strong evidence on discontinuation of DMTs, shared decision making and consideration of the ethical complexities could help in the decision-making process.
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 | 0.092 | 0.230 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".