Bibliographic record
Abstract
Multiple sclerosis (MS) is considered an inflammatory autoimmune disease of CNS white matter. Quantitative MRI measures of visible white matter lesion load, however, have shown that the burden of inflammatory disease is a relatively insensitive metric of physical disability, cognitive impairment, or long-term prognosis. While MRI has proven to be an invaluable tool for MS diagnosis and response to anti-inflammatory based therapies, 1 the relatively poor correlation between measures of inflammation and clinical sequelae has prompted revitalized recognition of the neurodegenerative aspects of MS. Pathologic studies have emphasized the extensive network of axonal transactions and neuronal cell loss in lesional and normal-appearing white matter (NAWM), and have confirmed that a significant aspect of MS pathology also resides in cortical and subcortical gray matter. Advances in MR imaging are ever-increasingly able to detect lesions in the cerebral mantle and in deep gray structures,2 gray matter loss can be detected even at the time of an initial demyelinating event,3 and apparently NAWM in patients with MS has been shown to have reduced structural integrity. …
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.185 | 0.129 |
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".