Prevalence of Cognitive Disorders in Patients with Systemic Lupus Erythromatosus; a Cross-sectional Study in Rasoul-e-Akram Hospital, Tehran, Iran.
Bibliographic record
Abstract
BACKGROUND: Neuropsychological manifestations are present in 60% of patients with Systemic Lupus Erythematosus (SLE) among which cognitive dysfunction is the most common. This study aims to determine the prevalence of cognitive disorders in SLE patients, and the relationship between cognitive disorder domains and depression and anxiety. METHODS: In this cross-sectional study, 54 patients with SLE and 48 healthy subjects were included. Mini-Mental State Examination (MMSE), Clock Drawing Test (CDT) and Trail Making Test part A (TMT-A) were used to screen for cognitive impairments. All subjects were evaluated with the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI) to determine depression and anxiety as probable confounding variables. RESULTS: The mean MMSE scores in SLE and control group patients (26.12 ± 3.58 and 28.01 ± 1.99, respectively) were significantly different (P = 0.001). The sub-scores in all areas assessed with MMSE were lower in SLE patients; however, it was only significant in the areas of orientation, recall and language (P < 0.05). SLE patients showed a significantly poorer performance in TMT compared to healthy controls (P = 0.01). The CDT according to the Watson scoring system showed significant difference between the two groups (P = 0.03). The Sunderland scoring system also indicated poorer performance in the SLE group, but the difference was not significant. CONCLUSION: Our study showed that cognitive disorders are more than 3-fold higher in SLE patients compared to normal subjects. The most impaired domains include orientation, Memory (recall), Language, Executive function, and psychomotor speed. Anxiety and depression are mostly correlated with domains included in the MMSE test.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".