White-matter Volume Reduction and the Protective Effect of Immunosuppressive Therapy in Systemic Lupus Erythematosus Patients with Normal Appearance by Conventional Magnetic Resonance Imaging
Bibliographic record
Abstract
OBJECTIVE: The central nervous system (CNS) is often affected by systemic lupus erythematosus (SLE), but assessment of CNS outcomes using noninvasive cerebral structural measures remains in its infancy. Magnetic resonance imaging (MRI) with expert visual interpretation is critical to diagnosis, but does not permit quantitative measurements. Our pilot study investigated whether quantitative brain volumetric analyses could be used to detect white-matter (WM) abnormalities and responses to treatment in SLE (ClinicalTrials.gov: NCT00703742). METHODS: Forty-two pairs of SLE patients and healthy controls underwent high-resolution 3-dimensional structural MRI scans. Combining voxel-based morphometry and region of interest analyses, subtle WM volume abnormalities in whole brains from SLE patients were identified, and regional WM volume was calculated. Associations between WM volume and symptom severity, as well as the effects of immunosuppressive therapy, were then investigated. RESULTS: The WM volume of the SLE group was significantly decreased in the bilateral posterior and anterior crus of the internal capsule (PIC and AIC, respectively), the subgyral right frontal lobe, and left temporal lobe (p < 0.001). Regional WM volume (left PIC and right AIC) was correlated with SLEDAI scores. The WM volume of patients treated with immunosuppressive therapy was greater than that of patients who were never treated with immunosuppressive therapy. CONCLUSION: Quantitative brain volumetric analyses detect brain injuries in WM for SLE that are not obvious by conventional MRI, and may be adequately sensitive and quantitative to measure the effect of therapeutic interventions in preventing brain injury and outcomes in SLE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".