T1 lesion load and cerebral atrophy as a marker for clinical progression in patients with multiple sclerosis. A prospective 18 months follow‐up study
Bibliographic record
Abstract
We investigated the relationship between local tissue destruction, diffuse cerebral atrophy and clinical progression in patients with established multiple sclerosis (MS). Twenty-nine patients with MS (13 patients with relapsing--remitting and 16 with secondary progressive disease) were included in a prospective serial study. Cerebral volumes, T1 hypointense lesion volumes, T2 hyperintense lesion volumes at baseline and at 18 months follow-up, and the volume of monthly enhancing lesions from month 0 to month 9 were assessed on magnetic resonance imaging (MRI) brain scans using highly reproducible semi-automated quantitative techniques. The main outcome measures were the MRI parameters and disability on Kurtzkes' Expanded Disability Status Scale. There was a significant correlation between the change (increase) in T1 lesion volume and progressive cerebral atrophy, whereas no correlation between the T2 lesion volume and atrophy was seen over the same follow-up period. The change in T1 lesion volume correlated more strongly than did T2 lesion volume change with the change in disability. We conclude that hypointense abnormalities detected in T1-weighted brain scans and cerebral atrophy may be directly linked. Although one should bear in mind some potential for reversibility due to inflammatory, oedematous lesions, these MR measures are a useful marker of progressive tissue damage and clinical progression in established MS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".