CHARACTERISTICS OF NEUROLOGICAL MANIFESTATIONS IN SYSTEMIC LUPUS ERYTHEMATOSUS AT A TERTIARY UNIVERSITY HOSPITAL
Bibliographic record
Abstract
PV208 / #631 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Neurological manifestations in systemic lupus erythematosus (SLE) are diverse and often underdiagnosed. At our tertiary university hospital, we proactively assess neurological involvement in patients with SLE using a structured protocol. This study aims to characterize these manifestations in a cohort of SLE patients. Methods From a total of 229 SLE patients, 12 (5.24%) met criteria for neuro-lupus. Systematic assessment included brain MRI, electroencephalogram (EEG), and, in selected cases, lumbar puncture. Diagnoses were established collaboratively with the Neurology department. Results Among the 12 neuro-lupus patients: • Peripheral neuropathy: 2 cases of axonal sensorimotor polyneuropathy were attributed to lupus. • Acute psychosis: 1 case, fully attributed to SLE activity after exclusion of other causes. • Lupus headache: 3 cases, classified based on current definitions. • Cognitive impairment: 6 cases ranged from moderate to severe, all with pathological MRI findings (Fazekas-type lesions). Treatment: • 2 patients received belimumab. • 3 were treated with anifrolumab. • 7 received rituximab. Conclusions Neurological manifestations in SLE are common (5.24% in our cohort) and likely underdiagnosed. A proactive clinical approach, combined with advanced imaging and collaboration with Neurology, facilitates timely identification and management of neuro-lupus.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".