The Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology and The MS Council for Clinical Practice Guidelines
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic recurrent inflammatory disorder of the central nervous system (CNS). The disease results in injury to the myelin sheaths, the oligodendrocytes and, to a lesser extent, the axons and nerve cells themselves (1-5). Women are affected more often than men. The disease typically becomes clinically apparent between the ages of 20 and 40 years, although, it can begin either earlier or later in life. In Canada, Europe, and the United States (US) the prevalence ranges from 100-200 cases per 100,000 population. The cause of MS is unknown although immune mediated mechanisms are almost certainly involved, either primarily or secondarily, and many authors favor a primary autoimmune basis for MS (5). MS is characterized pathologically by patches of demyelination that are found multifocally within the CNS white matter. Grey matter is relatively spared, as are the nerve axons although recent reports have highlighted the importance of axonal injury (4,6). There is considerable evidence indicating that autoreactive T-cells proliferate, cross the blood-brain barrier, and enter the CNS under the influence of cellular adhesion molecules and pro-inflammatory cytokines (7,8). In addition to T-cells, other mononuclear cells (macrophages and, to a lesser extent, B-cells) are also present in acute MS lesions. In chronic MS lesions, by contrast, the histological evidence of active inflammation is less conspicuous and lesions are characterized by gliosis as well as by a variable degree of axonal loss.
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 | 0.056 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.047 | 0.057 |
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".