P.139 Random Neural Network Features in Patients with Aggressive Multiple Sclerosis Undergoing Autologous Hematopoietic Stem Cell Transplant
Bibliographic record
Abstract
Background: Objective markers of disease progression are needed for patients with multiple sclerosis (MS). Increased randomness in neural networks is hypothesized to be an important cause of morbidity that can be objectified using graph theory. Methods: We use voxel-based structural similarity determined from T1-weighted MRI scans of 23 patients with MS receiving autologous stem cell transplant (ASCT) to compute cortical covariance network parameters. We examine associations between measures of cortical integration or segregation and biochemical/clinical measures of cortical health or function using Spearman correlation coefficients. P<0.05 was considered significant. Results: Path length increase was associated with markers of greater inflammation (ρ=0.56,P<.046) at baseline and reduced Naa/Cr ratio (P<.041) at 12 months. Reduced lambda was associated with markers of greater grey matter atrophy (ρ=0.55,P<.019) after 12 months and lower cognition (ρ=0.56,P<.008) at 12 months. Reduced clustering was associated with higher neurofilament (ρ=-0.68,P<.010) at baseline, greater white matter atrophy (ρ=0.62,P<.006) after 12 months, lower 2-second PASAT performance (ρ=0.56,P<.011) at baseline, and reduced Naa/Cr ratio (P<.001) at 12 months. Conclusions: Reduced cortical integration and segregation (random network features) co-occur with unfavourable markers of cortical health and function in patients with MS receiving ASCT. Network features show promise as important longitudinal markers of patient status and progression.
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 | 0.003 |
| 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) | 0.005 | 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".