Central Slab versus Whole Brain to Measure Brain Atrophy in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Structural Image Evaluation using Normalization of Atrophy (SIENA) is used to measure brain atrophy in multiple sclerosis (MS). However, brain extraction is prone to artefacts in the upper and lower parts of the brain. To overcome these shortcomings, some pivotal MS trials used a central slab instead of the whole brain as input for SIENA. The aim of this study was to compare the internal consistency and statistical dispersion of atrophy measures, associations with clinical outcomes and required sample sizes in clinical trials between these two approaches. METHODS: Brain volume change was assessed using SIENA in 119 MS patients with 5-years follow-up on 3D T1-weighted Magnetization Prepared Rapid Gradient Echo datasets using the whole brain or a central slab ranging from -10 to +60 mm Montreal Neurological Institute atlas coordinates. The statistical analysis included the quartile coefficient of dispersion, partial correlations with clinical outcomes and sample size calculations. Clinical outcome measures comprised the Expanded Disability Status Scale, MS Functional Composite and Symbol Digit Modalities Test. RESULTS: Annualized brain atrophy rates were higher using central slab than whole brain as input for SIENA (-0.51 ± 0.49 vs. -0.37 ± 0.39% per year, p < 0.001). Central and whole brain volume change showed comparable statistical dispersion and similarly correlated with clinical outcomes at 5-years follow-up. Sample size calculations estimated 14% fewer patients required to detect a given treatment effect when using the central slab instead of the whole brain option in SIENA. CONCLUSION: Central slab and whole brain SIENA produced comparable statistical dispersion with similar associations to clinical outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".