Proof of concept studies for tissue-protective agents in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: There is considerable interest in tissue-protective treatments for multiple sclerosis (MS). METHODS AND OBJECTIVES: We convened a group of MS clinical trialists and related researchers to discuss designs for proof of concept studies utilizing currently available data and assessment methods. RESULTS: Our favored design was a randomized, double-blind, parallel-group study of active treatment versus placebo focusing on changes in brain volume from a post-baseline scan (3-6 months after starting treatment) to the final visit 1 year later. Study designs aimed at reducing residual deficits following acute exacerbations are less straightforward, depending greatly on the anticipated rapidity of treatment effect onset. CONCLUSIONS: The next step would be to perform one or more studies of potential tissue-protective agents with these designs in mind, creating the longitudinal data necessary to refine endpoint selection, eligibility criteria, and sample size estimates for future trials.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".