Prevalence of cognitive impairment and cognitive improvement in patients with systemic lupus erythematosus during a 6-month follow-up study
Bibliographic record
Abstract
OBJECTIVES: The Montreal Cognitive Assessment (MoCA) is a simple and reliable screening tool for early detection for cognitive impairment in systemic lupus erythematosus (SLE). Most previous studies were cross-sectional with small samples. Research on long-term cognitive changes and reversibility is limited. This study aimed to establish the prevalence of cognitive impairment and changes in SLE patients after 6 months and the associated factors. METHODS: A prospective study was conducted in 200 patients with SLE between April 2021 and March 2022. Demographic data, disease activity, and medications were recorded. MoCA was administered at baseline and 6 months; for Thais, scores 17-24 indicate mild cognitive impairment, while ≤16 signifies severe impairment. Multivariate analysis identified factors associated with cognitive impairment and improvement. RESULTS: 12 years (OR, 3.11; 95% CI, 1.45-6.63), and prednisolone use (OR, 2.21; 95% CI, 1.08-4.51). Sixty-six (38.2%) of 173 patients completing the 6-month re-evaluation exhibited cognitive changes (52 [30.1%] improved; 14 [8.1%] deteriorated). Except for delayed recall, all commonly affected domains showed significant improvement. Disease activity, prednisolone, antimalarials, or immunosuppressant use did not predict cognitive improvement. CONCLUSIONS: Mild cognitive impairment is prevalent among patients with SLE. Due to the possibility of reversibility, early recognition and additional research to identify relevant factors are required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".