Cerebral Vasculopathy Is Associated with Severe Vascular Manifestations in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To investigate brain involvement in patients with systemic sclerosis (SSc). METHODS: Sixty-three patients with SSc fulfilling the American College of Rheumatology and/or Leroy and Medsger criteria were retrospectively studied, including 30 (47.6%) with limited cutaneous and 27 (42.9%) with diffuse cutaneous SSc. Forty-one patients underwent computed tomography (CT) scan and magnetic resonance imaging (MRI) of the brain, 11 patients only CT scan, and the remaining 11 patients only MRI. Cerebral vasculopathy on MRI and CT scan was defined as absent or mild (score < 1), moderate (1 or= 2) on a 4-point scale (0 to 3). RESULTS: Cerebral vasculopathy was identified on CT scan in 22 patients (moderate in 12 and severe in 10) and on MRI in 38 patients (moderate in 28 and severe in 10). Patients with severe cerebral vasculopathy seen on MRI were more likely to have pulmonary arterial hypertension (PAH; p = 0.003) and showed a tendency to have scleroderma renal crisis (SRC; p = 0.25, test for trend p = 0.097). A similar association was found between severe cerebral vasculopathy seen on CT scan and PAH (p = 0.026) or SRC (p = 0.04). After adjusting for age and hypertension, severe cerebral vasculopathy was still associated with increased risk of severe vascular manifestations [odds ratio (OR) 32, 95% confidence interval (CI) 3.45-297, p = 0.002 for CT scan; OR 26, 95% CI 1.71-394, p = 0.019 for MRI]. CONCLUSION: Severe cerebral vasculopathy is associated with severe vascular manifestations in SSc patients. SSc patients with severe vascular complications should undergo neuroradiological imaging assessment of brain involvement.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".