Using magnetic resonance imaging in animal models to guide drug development in multiple sclerosis
Bibliographic record
Abstract
Major advances are taking place in the development of therapeutics for multiple sclerosis (MS), with a move past traditional immunomodulatory/immunosuppressive therapies toward medications aimed at promoting remyelination or neuroprotection. With an increase in diversity of MS therapies comes the need to assess the effectiveness of such therapies. Magnetic resonance imaging (MRI) is one of the main tools used to evaluate the effectiveness of MS therapeutics in clinical trials. As all new therapeutics for MS are tested in animal models first, it is logical that MRI be incorporated into preclinical studies assessing therapeutics. Here, we review key papers showing how MR imaging has been combined with a range of animal models to evaluate potential therapeutics for MS. We also advise on how to maximize the potential for incorporating MRI into preclinical studies evaluating possible therapeutics for MS, which should improve the likelihood of discovering new medications for the condition.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".