A surface‐in gradient of thalamic damage evolves in pediatric multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Central nervous system pathology in multiple sclerosis includes both focal inflammatory perivascular injury and injury to superficial structures, including the subpial region of the cortex, which reportedly exhibits a gradient of damage from the surface inward. We assessed how early in the multiple sclerosis course a "surface-in" process of injury suggesting progressive biology may begin. METHODS: We focused on the thalamus, which notably has both a cerebrospinal fluid (CSF) interface and a white matter interface. Thalamic volume trajectories were assessed in a prospectively followed cohort of children from initial presentation with either multiple sclerosis or monophasic acquired demyelination, and healthy controls. Voxelwise volume changes were calculated using deformation-based morphometry, and analyzed in relation to distance from the CSF interface by mixed effects modeling and semiparametric smoothing methods. RESULTS: Twenty-seven children with multiple sclerosis and 73 children with monophasic demyelination were prospectively followed with yearly brain scans (mean follow-up = 4.6 years, standard deviation = 1.9). A total of 282 healthy children with serial scans were included as controls. Relative to healthy controls, children with multiple sclerosis and children with monophasic demyelination demonstrated volume loss in thalamic regions adjacent to the white matter. However, only children with multiple sclerosis exhibited an additional surface-in gradient of thalamic injury on the ventricular side, which was already notable in the first year of clinical disease (asymptote estimate = 3.01, 95% confidence interval [CI] = 1.44-4.58, p = 0.0002) and worsened over time (asymptote:time estimate = 0.33, 95% CI = 0.12-0.54, p = 0.0021). INTERPRETATION: Our results suggest that a multiple sclerosis disease-specific surface-in process of damage can manifest at the earliest stages of the disease. ANN NEUROL 2019;85:340-351.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".