Discrepancies in the interpretation of clinical symptoms and signs in the diagnosis of multiple sclerosis. A proposal for standardization
Bibliographic record
Abstract
The new McDonald diagnostic criteria for multiple sclerosis (MS) incorporate detailed criteria for the interpretation and classification of magnetic resonance imaging (MRI) findings, but, in contrast, provide no instructions for the interpretation of clinical findings. Because MS according to the McDonald criteria is one of the primary endpoints in a large trial enrolling patients after the first manifestation suggestive for a demyelinating disease (BENEFIT study), it was decided to organize a centralized eligibility assessment for this trial. During this eligibility assessment it was observed that there were marked inconsistencies in the decisions of participating neurologists with respect to the classification of clinical symptoms as being caused by one or more lesions provoking discussions in about one in every five patients. This paper describes these inconsistencies and their sources, and recommends a systematic approach that attempts to reduce the variability in interpreting clinical findings.
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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 | 0.545 | 0.525 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".