Disproportional smaller fornix with altered microstructure in pediatric multiple sclerosis shown by high-resolution fluid-suppressed diffusion tractography
Bibliographic record
Abstract
Background: Diffusion tensor imaging (DTI) in adults with multiple sclerosis (MS) has identified marked volume and diffusion abnormalities of the fornix, the main white matter (WM) output tract of the hippocampus. Objective: To determine if the fornix is affected in pediatric-onset MS (POMS) using the same DTI protocols used in adult-onset MS (AOMS), which would suggest its early involvement in the disease course. Methods: High-resolution, fluid-suppressed diffusion tractography was used to identify the fornix in 11 POMS patients (13-19 years old) and 26 controls. Fornix volume and diffusion metrics were compared between groups and with other total/regional brain volumes, and then correlated with cognitive/clinical scores. Results: POMS showed lower fornix volumes (-26%) compared to controls, which was greater than proportional losses in total and other regional brain volumes. Notably, the hippocampus volume was not lower in POMS. DTI yielded lower fractional anisotropy (-7%) and higher mean (+12%), axial (+7%), and radial (+16%) diffusivities in POMS. There were no significant correlations between fornix volume/diffusion metrics and cognitive/clinical scores. Conclusion: Diffusion tractography showed marked injury to the fornix in POMS that precedes injury to connected gray matter such as hippocampus, implicating the fornix as an early brain region affected in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".