Changes of cerebral structure and perfusion in subtypes of systemic sclerosis: a brain magnetic resonance imaging study
Bibliographic record
Abstract
OBJECTIVES: The characteristics of brain impairment in different subtypes of systemic sclerosis (SSc) (dcSSc, diffuse cutaneous SSc; lcSSc, limited cutaneous SSc) remain unclear. This study aimed to characterize cerebral structure and perfusion changes in different subtypes of SSc patients using magnetic resonance (MR) imaging. METHODS: Seventy SSc patients (46.0 ± 11.7 years, 62 females) and 30 healthy volunteers (44.8 ± 13.7 years, 24 females) were recruited and underwent brain MR imaging and Montreal Cognitive Assessment (MoCA) test. Gray matter (GM) volumes were measured using voxel-based morphometry analysis on T1-weighted images. Voxel-based and regional cerebral blood flow (CBF) was calculated on arterial spin labelling images. The cerebral structural and perfusion measurements by MR imaging were compared among dcSSc, lcSSc and healthy subjects using one-way ANOVA. The correlations between clinical characteristics and MR imaging measurements were also analysed. RESULTS: The dcSSc patients exhibited a significant reduction in GM volume in the para-hippocampal region (cluster P < 0.01, FWE corrected) compared with healthy volunteers. Whereas SSc patients, particularly lcSSc patients, showed elevated CBF in cerebellum, insula, cerebral cortex and subcortical structures (regional analyses: all P < 0.05; voxel-based analyses: cluster P < 0.01, FWE corrected). Furthermore, clinical characteristics of modified Rodnan skin score (mRSS) (r value ranged from -0.29 to -0.45), MoCA scores (r = 0.40) and anti-nuclear antibody (ANA) positivity (r = -0.33) were significantly associated with CBF in some regions (all P < 0.05). CONCLUSION: The manifestations of brain involvement vary among different subtypes of SSc. In addition, severe skin sclerosis may indicate higher risk of brain involvement in SSc patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".