Global loss of myelin water over 5 years in multiple sclerosis normal-appearing white matter
Bibliographic record
Abstract
Background: Reduced myelin water fraction (MWF, a marker for myelin), increased geometric mean T 2 (ieGMT 2 , reflecting intra/extracellular water properties), and increased T 1 (related to total water content) have been observed in cross-sectional studies of multiple sclerosis (MS) normal-appearing white matter (NAWM). Objective: To assess longitudinal changes of magnetic resonance (MR) measures in relapsing-remitting MS (RRMS) brain NAWM. Methods: A total of 11 subjects with RRMS and 4 controls were scanned on a 3T MRI at baseline and long-term follow-up (LTFU; 3.2–5.8 years) with a 32-echo T 2 relaxation and an inversion recovery T 1 sequence. For every voxel, MWF, ieGMT 2 , and T 1 were obtained. Mean, peak height, and peak location from NAWM mask-based histograms were determined. Results: In MS subjects, NAWM MWF mean decreased by 8% ( p = 0.0016). No longitudinal changes were measured in T 1 or ieGMT 2 . There was no relationship between change in any MR metric and change in EDSS. Control white matter showed no differences over time in any metric. Conclusion: The decreases we observed in MWF suggest that changes in myelin integrity and loss of myelin may be occurring diffusely and over long time periods in the MS brain. The timescale of these changes indicates that chronic, progressive myelin damage is an evolving process occurring over many years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".