Neuropsychiatric lupus and association with cerebrospinal fluid immunoglobulins: a pilot study.
Bibliographic record
Abstract
BACKGROUND: Recent experimental evidence points to brain-reactive antibodies as a key factor in the pathogenesis of neuropsychiatric systemic lupus erythematosus (central nervous system-SLE). However, clinical studies in which circulating (serum) autoantibodies were correlated with neuropsychiatric manifestations have not produced consistent findings. OBJECTIVES: To test the hypothesis that autoantibodies in cerebrospinal fluid are more reflective of functional brain damage. METHODS: We compared the behavioral profiles of 12 NP-SLE patients, some of whom had immunoglobulin G in their CSF. RESULTS: Western blotting revealed heavy and light chain IgG bands in six patients similar in age to the subgroup of CSF IgG-free patients. A series of serological measures did not differ between the subgroups, but SLEDAI scores and daily steroid doses were higher in patients with IgG in their CSF. All three patients with severe deficits in verbal and executive functions were positive for the CSF IgG, while three other patients with psychosis were CSF IgG-negative. CONCLUSIONS: Although the present sample size is relatively small, the results support the relationship between disease severity and central manifestations of autoimmunity. They also emphasize the importance of clinical studies that compare subpopulations of NP-SLE patients and justify development of animal models in which controlled immune mechanisms induce specific deficits in behavior.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".