Treating to target in multiple sclerosis: Do we know how to measure whether we hit it?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The rapidly evolving landscape of effective treatment options in multiple sclerosis has led to a shift of treatment objectives towards a treat-to-target approach aiming to suppress disease activity below the level of detectability early during the disease. To enable treat-to-target, a thorough reappraisal of available outcome measures with respect to their ability in this regard is required. METHODS: To that end, we conducted a comprehensive systematic literature review of more than 1000 studies using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 methodology focusing on underlying evidence as well as utility and implementability in clinical practice. RESULTS: From there, we propose a set of measurable outcomes for everyday routine clinical practice as well as advanced/aspirational measurables requiring additional resources. We also outline remaining knowledge/technology gaps that need to be overcome to enable a treat-to-target approach. CONCLUSIONS: This work provides the basis for an evidence-based definition of outcome targets for relevant stakeholders and regulatory authorities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".