Performance of Screening Tests for Cognitive Impairment in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: There is a need for a cognitive function screening test that can be administered to patients with systemic lupus erythematosus (SLE) in clinic. The objectives of this study were to determine (1) prevalence of cognitive impairment (CI) in SLE by the Montreal Cognitive Assessment (MoCA), Mini Mental State Examination (MMSE), in relation to the Hopkins Verbal Learning Test-Revised (HVLT-R), and Perceived Deficits Questionnaire 5-Item (PDQ-5); and (2) associated factors with CI. METHODS: Consecutive patients followed at a single center were recruited. HVLT-R, MoCA, and MMSE were administered. Sensitivity/specificity, positive (PPV)/negative (NPV) predictive values, and positive likelihood ratio (LR+) of MoCA/MMSE were determined (compared to HVLT-R). A test on intellectual ability and questionnaires on anxiety, depression, and perceived cognitive deficits were completed. Regression analyses determined associations with CI. RESULTS: Of 98 patients, 48% had CI using MoCA and 31% using HVLT-R. Sensitivity was higher for MoCA (73%) compared to MMSE (27%), though MMSE was more specific (90%) than MoCA (63%). PPV and LR+ were similar in MoCA and MMSE (PPV: 47%, 53%; LR+: 2.0, 2.6, respectively), but NPV was higher in MoCA (84%) than MMSE (74%). PDQ-5 predicted objective CI (HVLT-R: sensitivity 100%, specificity 89%). Although CI was associated with depression in univariate analyses, it did not hold in the multivariate analysis, while longer SLE disease duration and more years of education remained significant. CONCLUSION: CI is highly prevalent and MoCA may be a useful tool to screen for CI in SLE. Patients with more years of education were less likely to have CI.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".