Evaluation of the Montreal Cognitive Assessment as a screening tool for cognitive dysfunction in SLE
Bibliographic record
Abstract
OBJECTIVES: Cognitive dysfunction in SLE is common and associated with significant morbidity but is currently underdetected. Early detection requires the use of screening tests, as formal diagnostic cognitive testing is time-consuming. This study aims to evaluate the Montreal Cognitive Assessment (MoCA) as a screening tool for cognitive dysfunction in SLE. METHODS: Patients with SLE (n=95) and demographically matched healthy control participants (n=48) underwent cognitive testing using the 1-hour neuropsychiatric test battery recommended by the American College of Rheumatology for use in SLE and the MoCA. We used regression analyses to determine associations between MoCA and cognitive test scores. We assessed several MoCA cut-offs for predicting cognitive impairment in terms of sensitivity, specificity, positive predictive value and negative predictive value. Receiver operating curve analyses were used to determine the diagnostic accuracy of the MoCA cut-off thresholds. RESULTS: We found a significant correlation between MoCA score and 9 of the 10 cognitive endpoints studied (all p<0.001). Receiver operating curve analysis suggested that a MoCA cut-off of <27 had highest diagnostic accuracy across the cognitive impairment definitions (area under the curve 0.76-0.78). Using a screening cut-off of <28, the MoCA had sensitivity of 83%-94% and specificity of 46%-59%, depending on the impairment definition used. CONCLUSIONS: The MoCA correlates strongly with cognitive test results in SLE and has sufficient sensitivity for use as a screening tool with a cut-off of <28 as the optimal threshold. This tool can be incorporated into clinical practice for screening for cognitive dysfunction in SLE.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".