A multimodal evaluation of early cognitive dysfunction in a cohort of systemic lupus patients
Bibliographic record
Abstract
Background Cognitive dysfunction in systemic lupus erythematosus (SLE) is common yet often flies under the radar, subtle in onset and frequently overlooked in clinical practice. This study sheds light on the cognitive toll of SLE, mapping the domain-specific impairments and probing their links to clinical features, anti-NR2 antibodies, and structural brain changes. Patients and methods We assessed 100 female SLE patients using the Montreal Cognitive Assessment (MoCA). Those scoring less than or equal to 25 were classified as having mild cognitive impairment (MCI). Clinical, serological (including anti-NR2 antibodies), and neuroimaging data were analyzed. A subset of 23 MCI patients underwent volumetric MRI to explore brain structural correlates, focusing on hippocampal and caudate atrophy. Results Nearly one in four (24%) patients showed cognitive impairment. Compared with cognitively intact peers, MCI patients more frequently had arthritis, leukopenia, and proteinuria. MoCA domain analysis revealed widespread deficits, particularly in visuospatial/executive function (91.7%), attention (79.2%), and language (50%). Anti-NR2 antibodies were strikingly elevated in the MCI group (79.2 vs. 17.1%, P < 0.001) and strongly correlated with lower MoCA scores (ρ=–0.490, P < 0.001). MRI revealed hippocampal atrophy in 17.4% and caudate atrophy in 8.7% of MCI patients-partially mirroring their cognitive profile. Conclusion Cognitive impairment in SLE is not only prevalent but also patterned, affecting key domains of executive function and attention. Elevated anti-NR2 antibodies and hippocampal volume loss emerge as promising biomarkers, offering a window into the mechanisms of lupus-related cognitive decline, and potentially guiding earlier intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".