MRI T2 lesion burden in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown only modest correlation between multiple sclerosis (MS) lesions on MRI and clinical disability. OBJECTIVE: To investigate the relationship between proton density/T2-weighted (T2) burden of disease (BOD) quantitatively measured on MRI scans and clinical determinants including disability. METHODS: Using the Sylvia Lawry Centre for Multiple Sclerosis Research (SLCMSR) database, the authors studied baseline T2 BOD data from a pooled subsample of 1,312 placebo MS patients from 11 randomized controlled trials. Univariate comparisons guided development of multiple regression models incorporating the most important clinical predictors. RESULTS: Significant, although weak to moderate, correlations were found between T2 BOD and age at disease onset, disease duration, disease course, disability (as measured by the Expanded Disability Status Scale [EDSS]), relapse rate, certain presenting symptoms, and gadolinium enhancement. An unexpected but key finding that persisted in the multiple regression analyses was a plateauing relationship between T2 BOD and disability for EDSS values above 4.5. CONCLUSIONS: This study confirmed the limited correlation between clinical manifestations and T2 burden of disease (BOD) but revealed an important plateauing relationship between T2 BOD and disability.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".