Nervous System Disease in Systemic Lupus Erythematosus: Current Status and Future Directions
Bibliographic record
Abstract
The American College of Rheumatology's case definitions for 19 neuropsychiatric syndromes in systemic lupus erythematosus (SLE) constitute a comprehensive classification of nervous system events in this disease. However, additional strategies are needed to determine whether a neuropsychiatric syndrome is attributable to SLE versus a competing comorbidity. Cognitive function is a clinical surrogate of overall brain health, with applications in both diagnosis and determination of clinical outcomes. Ischemic and inflammatory mechanisms are both key components of the immunopathogenesis of neuropsychiatric SLE (NPSLE), including abnormalities of the blood-brain barrier and autoantibody-mediated production of proinflammatory cytokines. Advances in neuroimaging provide a platform to assess novel disease mechanisms in a noninvasive way. The convergence of more rigorous clinical characterization, validation of biomarkers, and brain neuroimaging provides opportunities to determine the efficacy of novel targeted therapies in the treatment of NPSLE.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".