Screening for Cognitive Dysfunction in Systemic Lupus Erythematosus: a Systematic Review
Bibliographic record
Abstract
Background: Screening instruments are brief and simple tools designed to identify alterations in cognitive functioning, as well as to rule out or confirm the presence of cognitive dysfunction. Systemic lupus erythematosus (SLE) is an inflammatory disease that affects multiple organs of the human body, including the nervous system, which can cause neuropsychiatric symptoms such as cognitive dysfunction. Objective: To analyze the scientific evidence on cognitive screening tests used in SLE and the cutoff points suggested in academic literature. Methodology: A systematic review was conducted from 2014 to the second half of 2024, in databases such as PubMed, Scopus, Web of Science, ScienceDirect, Redalyc and SciELO, based on combinations of keywords and Boolean operators: "cognitive dysfunction", "cognitive impairment", "cognitive decline", "lupus", "systemic lupus erythematosus", "SLE", "Montreal Cognitive Assessment", "MoCA", “MMSE”, “Mini-Mental State Examination”, “INECO Frontal Screening", "INECO", "Addenbrooke's Cognitive Examination", and "ACE-R III". Results: The search yielded a total of 564 works, of which 31 documents met the inclusion criteria and were analyzed. The results show that the MoCA is the preferred tool by physicians and specialists worldwide for cognitive screening in patients with SLE, both in patients with and without clear neuropsychiatric symptoms. Conclusion: The literature consistently supports a cutoff score of <26 on the MoCA as the most widely used threshold for identifying cognitive dysfunction in individuals with systemic lupus erythematosus, with this criterion receiving the strongest empirical validation across published studies. However, some studies have explored lower cutoff scores to improve the instrument's specificity and sensitivity, especially in the SLE population.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".