Frequency and Diagnostic Implications of Paramagnetic Rim Lesions in People Presenting for Diagnosis to a Multiple Sclerosis Clinic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Paramagnetic rim lesions (PRLs) are a well-established imaging biomarker of chronic active multiple sclerosis (MS) lesions. PRLs have been shown to be highly specific for MS (∼90% specificity), and their prevalence has been estimated to be approximately 50% in patients with clinically established diagnoses of MS. In this study, we evaluated the frequency and diagnostic value of PRLs in patients at first clinical presentation. METHODS: Adults age 18-64 years presenting with clinical symptoms or radiologic suspicion of demyelinating disease referred to academic specialty MS centers without a definitive diagnosis were prospectively enrolled in a multicenter, cross-sectional, observational study. Phase images from high-resolution 3D echo-planar imaging were acquired on 3-tesla brain MRI and evaluated for PRLs by 3 independent raters, blinded to diagnosis, with adjudication from a fourth expert rater. Diagnostic performance of PRLs for a diagnosis of MS using the 2017 McDonald criteria as gold standard was evaluated using diagnostic thresholds based on the presence of at least 1 PRL (≥1 PRL) or at least 2 PRLs (≥2 PRLs). RESULTS: = 0.03). DISCUSSION: PRLs are highly prevalent early in patients with MS at the time of first clinical presentation and can differentiate MS from mimics with high accuracy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".