Positive cerebrospinal fluid in the 2024 McDonald criteria for multiple sclerosis
Bibliographic record
Abstract
The 2024 McDonald diagnostic criteria for Multiple Sclerosis (MS) introduce kappa free light chains (κ-FLC) detection in cerebrospinal fluid (CSF) which can be used interchangeably with oligoclonal IgG bands (OCB) to demonstrate intrathecal immunoglobulin synthesis. Diagnostic sensitivity and specificity of κ-FLC is equal to OCB on a 95% confidence level. In rare cases determination of both, κ-FLC and OCB should be considered as the concordance rate is around 90%. We recommend calculating the κ-FLC index with values of ≥6.1 performing best for diagnosing MS. Validated turbidimetric or nephelometric assays should be applied for which proficiency testing programs are available. There is some prognostic use of the κ-FLC index with higher values predicting higher disease activity. Neurofilament light (NfL) should not be used for diagnostic purposes although it might be useful for prognosis and disease monitoring. All recommendations apply to paediatric and adult relapsing as well as progressive onset MS.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".