Diagnostic accuracy of the Montreal Cognitive Assessment in screening for cognitive impairment in initially hospitalized COVID-19 patients: Findings from the prospective multicenter NeNeSCo study
Bibliographic record
Abstract
Objective: This study aimed to investigate the prevalence and nature of cognitive impairment among severely ill COVID-19 patients and the effectiveness of the Montreal Cognitive Assessment (MoCA) in detecting it. Method: We evaluated cognition in COVID-19 patients hospitalized during the first wave (March to June 2020) from six Dutch hospitals, nine months post-discharge, using a comprehensive multi-domain neuropsychological test battery. Test performance was corrected for sex, age, and education differences and transformed into z-scores. Scores within each cognitive domain were averaged and categorized as average and above (z-score ≥ -0.84), low average (z-score -1.28 to -0.84), below average (z-score -1.65 to -1.28), and exceptionally low (z-score < -1.65). Patients were classified with cognitive impairment if at least one domain's z-score fell below -1.65. We assessed the MoCA's accuracy using both the original cutoff (<26) and an "optimal"cutoff determined by Youden's index. Results: Cognitive impairment was found in 12.1% (24/199) of patients, with verbal memory and mental speed most affected (6.5% and 7% below -1.65, respectively). The MoCA had an area under the curve of 0.84. The original cutoff showed sensitivity of 83% and specificity of 66%. Using the identified optimal cutoff of <24, maintained sensitivity while improving specificity to 81%. Conclusions: Cognitive impairment prevalence in initially hospitalized COVID-19 patients is lower than initially expected. Verbal memory and processing speed are primarily affected. The MoCA is a valuable screening tool for these impairments and lowering the MoCA cutoff to <24 improves specificity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".