Prevalence, involved domains, and predictor of cognitive dysfunction in systemic lupus erythematosus
Bibliographic record
Abstract
Background Cognitive Dysfunction (CD) can occur in Systemic Lupus Erythematosus (SLE) before the occurrence of Neuropsychiatric Lupus Erythematosus (NPSLE). Given the reversibility and fluctuation of SLE-related CD, the research for possible predictors is of great significance for early detection and intervention. Objective We sought to determine the prevalence, involved domains, and possible predictors of CD in SLE patients. Methods We conducted a retrospective cross-sectional study at Nanfang Hospital from 2018 to 2019. A total of 78 SLE patients were recruited. The Montreal Cognitive Assessment (MoCA) scale was used to screen cognitive function. Demographic, clinical, and laboratory characteristics were collected. The serum anti-methyl-d-aspartate receptor (anti-NMDAR) antibody and S100β were measured by enzyme-linked immunosorbent assay (ELISA). Multivariate logistic regression analysis and ROC curve were used to assess the predictor of SLE-related CD. Results Of 78 recruited patients,53 (67.9%) had CD. It mainly involved delayed recall, abstract generalization, verbal repetition, and fluency. The disease activity index (SLEDAI) was not associated with SLE-related CD ( p > 0.05). Multivariate logistic regression showed that an increase in each year of education there was a decrease in the likelihood of CD (OR 0.261, CI 0.080-0.857, p = 0.027) whereas with each unit increase in serum anti-NMDAR antibody there was an increased likelihood of SLE-related CD (OR 1.568, CI 1.073–2.292, p = 0.020). Conclusion The prevalence of SLE-related CD was 67.9% in our study and SLE-related CD was not associated with disease activity. Serum anti-NMDAR antibody can be used as a predictor for SLE-related CD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".