A roadmap to precision medicine for multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) affects approximately 1 million persons in the United States, and is the leading cause of neurological disability in young adults. The concept of precision medicine is now being applied to MS and has the promise of improved care. MS patients experience a variety of neurological symptoms, and disease severity ranges from mild to severe, and the biological underpinnings of these phenotypes are now starting to be elucidated. Precision medicine involves the classification of disease subtypes based on the underlying biology, rather than clinical phenotypes alone, and may govern disease course and treatment response. Over 18 disease-modifying drugs have been approved for the treatment of MS, and several biomarkers of treatment response are emerging. This article provides an overview of the concepts of precision medicine and emerging biological markers and their evolving role in decision-making in MS management.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".