Primary progressive multiple sclerosis: cerebrospinal fluid considerations
Bibliographic record
Abstract
Diagnosing the 'primary progressive' form of multiple sclerosis (PPMS) requires assurance that other conditions that might cause a chronic inflammatory neurodegenerative central nervous system (CNS) disease have been ruled out. Both imaging and pathological studies have shown that this form of MS tends to be less inflammatory compared with either the relapsing-remitting or secondary progressive types. There are therefore many conditions that cause a slowly progressive wasting of the CNS that might be confused with MS. The new MS diagnostic scheme has made the presence of 'typical' MS abnormalities in the cerebrospinal fluid (CSF) a mandatory first criterion, but there may well be individuals that still have PPMS even in the absence of a typical MS CSF. Here we explore what the CSF can tell about an individual's disease process and outline the current state of the art in terms of CSF analysis. Used properly, the CSF can be very helpful in clarifying a diagnosis of PPMS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".