P.071 Multi-parametric MRI at 7 T enables differentiation of MS and age-related white matter lesions
Bibliographic record
Abstract
Background: MRI criteria are used to support multiple sclerosis diagnosis and evolution. However, normal age-related lesions (ARLs) can be cofounded with MS white matter lesion (MSL). Methods: Two Multiparametric 7T MRI scans 4 motnhs apart from 5 relapsing MS (RMS) patients were analyzed and compared to 5 matched healthy controls (HC) aiming to differentiate MSLs from ARLs. Six-echo GRE, FLAIR and MPRAGE sequences were acquired. Results: Average size of ARLs was 51 mm3 and of MSLs was 69 mm3 (p=0.27). Both have the same general appearance on FLAIR and MPRAGE contrasts, but different contrast on the R2* and QS maps. Inter-visit variation on MPRAGE was significantly higher in MSLs. Inter-visit signal change in the other contrasts (QSM, R2* and FLAIR) was not significant. Conclusions: R2*, QS maps and inter-visit variation using MPRAGE allowed differentiating MSLs from ARLs in 5 RMS with mean long term disease duration. This could improve correct early diagnosis and accurate lesion load accumulation evolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".