The relation of focal white matter signal abnormality and focal volume loss in multiple sclerosis
Bibliographic record
Abstract
There were two aims to this study. First, to explore how the reduction in the volume of abnormal T2-signal intensity associated with white matter (WM) lesions in multiple sclerosis (MS) relates to tissue loss resulting from focal pathology inside lesions. Second, to demonstrate that this volume of abnormal T2-signal intensity underestimates the actual size of the region to which the direct effects of lesion activity extend. For these purposes, we used deformation field analysis to quantify the evolution of local atrophy associated with a chronic peri-ventricular lesion in a patient with secondary progressive MS. This subject had particular features that may not necessarily co-exist in a group of unselected patients, which enabled interesting observations to be made. We show, quantitatively, that the focal WM lesion was associated with adjacent regional WM volume loss, which was disproportionate to concurrent diffuse atrophy in the rest of the normal appearing brain tissue, and that the loss of volume associated with the lesion was partially reciprocated by local ventricular expansion. Our observations re-emphasise the complex relationship between the change in the volume of abnormal signal intensity on magnetic resonance images and the tissue volume change directly related to lesion pathology.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".