Atrophy in white matter fiber tracts in multiple sclerosis is not dependent on tract length or local white matter lesions
Bibliographic record
Abstract
The pathogenesis of tissue injury outside the white matter (WM) plaques of multiple sclerosis (MS) has not yet been clearly defined. To better understand the pathogenesis of this injury and the associated atrophy, we investigated volume loss over time in 20 WM fiber tracts. We defined two main aims: (1) to examine whether certain fiber tracts were more prone to atrophy, and to test the possible relation of tract atrophy to tract length and selected MS-specific variables; and (2) to investigate the possible relation of atrophy to lesion load (whole brain and in the specific tract). Local volume change was assessed between two distant time points for each MS patient studied. Fiber tracts were segmented automatically using a tractography-based atlas. Results demonstrate volume loss in all fiber tracts. The uncinate fasciculus and anterior-thalamic radiation had the greatest yearly percentage atrophy. Disease type, duration, median expanded disability status scale, total lesion load, and gender exhibited significant effects on atrophy in at least one tract. Together, these data are more consistent with a pathogenesis for the degeneration related to diffuse inflammation rather than the secondary effects of focal lesions.
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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 | 0.001 |
| 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 | 0.000 | 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".