Predictors of Incident Seizure in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: The risk factors for incident seizures in systemic lupus erythematosus (SLE) were prospectively determined in a cohort study. METHODS: A total of 2203 patients with SLE followed longitudinally in the Hopkins Lupus Cohort were analyzed. Demographic variables, clinical manifestations, laboratory tests, and SLE disease activity were recorded at each quarterly visit. Adjusted estimates of association of risk factors for onset of seizure were derived using pooled logistic regression. We examined incident seizures in 3 ways: at the time of diagnosis, more than 45 days after the diagnosis of SLE, and after cohort entry. RESULTS: Of 2203 patients with no history of seizure prior to SLE diagnosis, 157 (7.13%) had the first seizure occurrence at the time of (37 patients, 1.68%) or after diagnosis (120 patients, 5.45%) of SLE. The risk of seizure occurring around the time of SLE diagnosis was higher in patients with a history of malar rash (p = 0.002), proteinuria (p = 0.004), and psychosis (p < 0.001). Multivariable analysis of the first seizure occurring after the diagnosis of SLE showed that history of low C3 (p = 0.0078), psychosis (p < 0.0001), cranial or peripheral neuropathy (p = 0.0043), anti-Sm antibody (p = 0.0551), renal involvement (p = 0.0177), and current corticosteroid dose (p < 0.0001) were independently associated with a higher incidence of seizure. Disease activity was not predictive after adjusting for corticosteroids. CONCLUSION: Risk of seizure after diagnosis of SLE is increased in those patients with prior psychosis, neuropathy, proteinuria, anti-Sm, low C3, and use of corticosteroids.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".