Silent lesions on MRI imaging – Shifting goal posts for treatment decisions in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: The current best practice suggests yearly magnetic resonance imaging (MRI) to monitor treatment response in multiple sclerosis (MS) patients. OBJECTIVE: To evaluate the current practice of clinicians changing MS treatment based on subclinical new MRI lesions alone. METHODS: Using MSBase, an international MS patient registry with MRI data, we analysed the probability of treatment change among patients with clinically silent new MRI lesions. RESULTS: A total of 8311 MRI brain scans of 4232 patients were identified. Around 26.9% (336/1247) MRIs with one new T2 lesion were followed by disease-modifying therapy (DMT) change, increasing to 50.2% (129/257) with six new T2 lesions. DMT change was twice as likely with new T1 contrast enhancing compared to new T2 lesions odds ratio (OR): 2.43, 95% confidence interval (CI): 2.00-2.96 vs OR: 1.26 (95% CI: 1.22-1.29). DMT change with new MRI lesions occurred most frequently with 'injectable' DMTs. The probability of switching therapy was greater only after high-efficacy therapies became available in 2007 (after, OR: 1.43, 95% CI: 1.28-1.59 vs before, OR: 0.98, 95% CI: 0.520-1.88). CONCLUSION: MS clinicians rely increasingly on MRI alone in their treatment decisions, utilizing low thresholds (1 new T2 lesion) for optimizing MS therapy. This signals a shift towards no evidence of disease activity (NEDA)-3 since high-efficacy therapies became available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".