Prevalence of cognitive impairment using the Montreal Cognitive Assessment questionnaire among patients with systemic lupus erythematosus: a cross-sectional study at two tertiary centres in Malaysia
Bibliographic record
Abstract
Introduction Cognitive impairment is a common neuropsychiatric manifestation of systemic lupus erythematosus (SLE). However, it is not routinely assessed for despite its high prevalence and significant disease burden. Aims This study aimed to determine the prevalence of mild cognitive impairment (MCI) using the Montreal Cognitive Assessment (MoCA) and its associated factors among patients diagnosed with SLE in Malaysia. Methods A total of 200 SLE patients were recruited prospectively from the outpatient clinics of two tertiary hospitals in Malaysia. Standardized clinical interview was utilized to obtain information on socio-demographic characteristics. All patients were then assessed using the MoCA questionnaire for presence of cognitive impairment; the Patient Health Questionnaire 9 (PHQ-9) for presence of depressive symptoms; and the Wong–Baker Faces Pain Scale (WBFPS) for severity of pain. The evaluation of disease activity and severity were performed by the treating rheumatologists and nephrologists using the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and Systemic Lupus International Collaborating Clinics Damage Index (SLICC DI). Results The prevalence of MCI was 35%. The significant associated factors from the bivariate analysis were male gender ( p = 0.04), educational level ( p = 0.00), WBFPS score ( p = 0.035) and anticardiolipin IgM ( p = 0.01). Further analysis using logistic regression model found that male gender (OR = 7.43, 95% confidence interval 1.06–52.06, p = 0.04), lower educational level (OR = 4.4, 95% confidence interval 1.47–13.21, p = 0.01) and presence of anticardiolipin IgM (OR = 6.81, 95% confidence interval 1.45–32.01, p = 0.031) were associated with impaired MoCA scores. Also, increasing pain scores increased the risk of patients being affected by cognitive impairment. Conclusion Over one-third of patients with SLE in our cohort were found to have MCI. Risk factors included male gender, lower educational level, higher pain score and presence of anticardiolipin IgM. Physicians are encouraged to perform routine screening to detect cognitive dysfunction in patients with SLE in their clinical practice as part of a more comprehensive management.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".