International consensus statement on the use of disease-modifying agents in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To provide recommendations on the use of disease-modifying agents in the management of multiple sclerosis (MS) and to ensure that treatment will be available to those patients who may benefit. METHODS: An initial draft of the consensus statement was prepared by the Steering Committee and amended in the light of written comments from a group of MS specialists. At a subsequent workshop, the wording of the consensus statement was discussed, modified if necessary, and the participants indicated their level of support using an electronic voting system. A new draft of the statement was then sent to a much larger group of international opinion leaders in MS for further comment. RESULTS: A number of statements were agreed, which outline the criteria for consideration of disease-modifying therapy for MS and recommendations for treatment. Each statement was accepted completely, or with only minor reservations by 95% or more of those present at the workshop. CONCLUSIONS: Periodic reviews and modifications to the statement will be required, as new approaches to the treatment of MS and other therapeutic agents become available.
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 | 0.123 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".