Multiple sclerosis: clinical aspects
Bibliographic record
Abstract
PURPOSE OF REVIEW: Multiple sclerosis is a chronic, predominantly immune-mediated disease of the central nervous system, and one of the most common causes of neurological disability in young adults globally. This review will discuss the epidemiology, diagnosis, disease course, and prognosis of multiple sclerosis and will focus on recent evidence and advances in these aspects of the disease. RECENT FINDINGS: Multiple sclerosis is increasing in incidence and prevalence globally, even in traditionally low-prevalence regions of the world. Recent revisions have been proposed to the existing multiple sclerosis diagnostic criteria, which will facilitate earlier diagnosis and treatment in appropriate patients. Classifying multiple sclerosis into distinct disease phenotypes can be challenging, and recent refinements have been proposed to clarify existing definitions. The prognosis of multiple sclerosis varies substantially across individual patients, and a combination of clinical, imaging, and laboratory markers can be useful in predicting clinical course and optimizing treatment in individual patients. SUMMARY: A number of recent advances have been made in the clinical diagnosis and prognostication of multiple sclerosis patients. Future research will enable the development of more accurate biomarkers of disease categorization and prognosis, which will enable timely personalized treatment in individual multiple sclerosis patients.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".