Recommended standard of cerebrospinal fluid analysis in the diagnosis of multiple sclerosis: a consensus statement.
Bibliographic record
Abstract
New criteria for the diagnosis of multiple sclerosis (MS) were published as the result of an internationally formed committee. To increase the specificity of diagnosis and to minimize the number of false diagnoses, the committee recommended the use of both clinical and paraclinical criteria, the latter involving information obtained from magnetic resonance imaging, evoked potentials, and cerebrospinal fluid (CSF) analysis. Although rigorous magnetic resonance imaging requirements were provided, the "new criteria paper" fell short in terms of guidelines as to how the CSF analysis should be performed and simply equated the IgG index with isoelectric focusing, without any justification. The spectrum of parameters analyzed and methods for CSF analysis differ worldwide and often yield variable results in terms of sensitivity, specificity, accuracy, and reliability, with no decided "optimal" CSF test for the diagnosis of MS. To address this question specifically, an international panel of experts in MS and CSF diagnostic techniques was convened and the result was this article, representing a consensus of all the participants. These recommendations for establishing a standard for the evaluation of CSF in patients suspected of having MS should greatly complement the new criteria in ensuring that a correct diagnosis of MS is being made.
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 | 0.087 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.014 | 0.004 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".