Predictive value of total CSVD burden scores in cognitive impairment among SLE patients on the basis of MRI evaluation
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment (CI) is a significant problem in systemic lupus erythematosus (SLE) patients. In recent years, total cerebral small vessel disease (CSVD) burden scores have had substantial value in predicting cognitive impairment. However, its application in treating concurrent cognitive impairment in SLE patients is unclear. To explore the relationship between total CSVD burden scores and cognitive dysfunction in SLE patients and to analyze its predictive value. METHODS: The Montreal Cognitive Assessment (MoCA) score was used to evaluate the cognitive function of 50 patients with SLE, and the total load score of patients with CSVD was analyzed via magnetic resonance imaging (MRI). Multivariate regression was used to evaluate the relationship between total CSVD burden scores and cognitive dysfunction, and the predictive value of total CSVD burden scores was assessed. RESULTS: Multivariate logistic regression analysis revealed that years of education (OR = 0.975, 95% CI [0.952-0.998], P = 0.035), neuropsychiatric systemic lupus erythematosus (NPSLE) (OR = 4.152, 95% CI [2.158-7.990], P < 0.001), and the CSVD total burden score (OR = 3.884, 95% CI [0.840-0.928], P < 0.001) were independently associated with cognitive impairment in SLE patients. The results of the ROC curve analysis revealed that the area under the curve (AUC) of the CSVD total burden score for the prediction of cognitive impairment in SLE patients was 0.885. CONCLUSIONS: Years of education, NPSLE score, and total CSVD burden score are closely related to the occurrence of cognitive impairment in SLE patients. In particular, the total CSVD burden score is beneficial for the prediction of cognitive impairment. CLINICAL TRIAL NUMBER: Not applicable. TRIAL REGISTRATION: Not applicable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".