Correlation Between Cognitive Function with Disease Activity of Systemic Lupus Erythematosus Patients in Dr. Hasan Sadikin Hospital Bandung: An Analytical Cross-Sectional Study
Bibliographic record
Abstract
Background: Cognitive dysfunction was found in 55-80% Neuropsychiatry Systemic Lupus Erythematosus (NPSLE) patients. Serious concern from clinicans was needed as its impact to patient’s quality of life. Disease activity is expected to be affecting patient’s cognitive function. Previous studies regarding correlation between disease activity and cognitive dysfunction showed various results. This study aimed to evaluate the correlation between disease activity and cognitive function in SLE patients.Methods: This study is an analytical cross-sectional study. Subjects were SLE patients at the rheumatology clinic of Dr. Hasan Sadikin Hospital Bandung during June-August 2017. Subject’s evaluations included disease activity assessment using SLE disease activity index-2K (SLEDAI-2K) and cognitive function assessment using MoCA-Ina test. Data were analyzed by using Spearman Rank correlation test. Results: Mean age of the subjects was 31 ± 8 years old, most of them were senior high school graduates (65.8 %) and median length of study was 12 years. Subject’s median duration of illness was 44 months. Their MoCA-Ina median score was 25, while SLEDAI-2K median score was 6. Cognitive dysfunctions were found in more than half of subjects (52.63%), which memory domain (78.95%) was most frequently impaired. Most of subjects were patients with active SLE (63.2%). Correlation test showed there was no correlation between SLEDAI-2K score and MoCA-Ina score (rs=0.023, p=0.445).Conclusion: There was no correlation between disease activity (SLEDAI-2K score) and cognitive function (MoCA-Ina score). Keywords: Cognitive dysfunction, MoCA-Ina, Systemic lupus erythematosus, SLEDAI-2K
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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 | 0.001 | 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".