Brain Magnetic Resonance Imaging in Newly Diagnosed Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: We wished to determine the prevalence of cerebral atrophy and focal lesions in a cohort of patients with newly diagnosed systemic lupus erythematosus (SLE) and the association of these brain abnormalities with clinical characteristics. METHODS: A total of 97 patients with SLE, within 9 months of diagnosis, with 4 or more American College of Rheumatology classification criteria, were enrolled. Brain magnetic resonance imaging was performed. RESULTS: The patients were 97% female, mean age 38.1 (SD 12.2) years, education 15.1 (2.8) years; 59 Caucasian, 11 African American, 19 Hispanic, 5 Asian, and 3 other ethnicity. Cerebral atrophy was prevalent in 18% (95% CI 11%-27%): mild in 12%, moderate in 5%. Focal lesions were prevalent in 8% (95% CI 4%-16%): mild in 2%, moderate in 5%, severe in 1%. Patients with cerebral atrophy were more likely to have anxiety disorder (p = 0.04). Patients with focal lesions were more likely to be African American (p = 0.045) and had higher Safety of Estrogens in Lupus Erythematosus National Assessment SLEDAI scores (p = 0.02) and anti-dsDNA (p = 0.05). CONCLUSION: In this population with newly diagnosed SLE, brain abnormalities were prevalent in 25% of patients. These findings suggest that the brain may be affected extremely early in the course of SLE, even before the clinical diagnosis of SLE is made. Followup of these patients is planned, to determine the reversibility or progression of these abnormalities and their association with and potential predictive value for subsequent neuropsychiatric SLE manifestations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".