The Prognostic Test Accuracy of the Short and Standard Forms of the Montreal Cognitive Assessment
Bibliographic record
Abstract
INTRODUCTION: Cognitive impairment is a critical concern in stroke care, and international guidelines recommend early cognitive screening. The aim of this study was to determine the prognostic accuracy of both the short and standard forms of the Montreal Cognitive Assessment (MoCA) in predicting long-term cognitive recovery following a stroke. METHODS: For this study, we used data from the Efficacy of Fluoxetine - a Randomized Controlled Trial in Stroke (EFFECTS) study, which encompassed stroke patients from 35 Swedish centers over the period from 2014 to 2019. Cognitive assessments were initially conducted at 2-15 days post-stroke, with follow-up data gathered at 6 months. We used the MoCA for objective cognitive evaluation. For assessing subjective cognitive impairment, we used the memory and thinking domain of the Stroke Impact Scale. For psychometric evaluation of the short Swedish version of MoCA (s-MoCA-SWE), we used cross tables and binary logistic regression. RESULTS: The study included 1,141 patients (62.2% men; median [interquartile range; IQR] age, 72.3 [13.2] years; median [IQR] stroke severity, 3.0 [3.0]). At baseline, the prevalence of cognitive impairment was 71.7% according to the s-MoCA-SWE (≤12) and 67.0% according to the MoCA (≤25). The s-MoCA-SWE demonstrated a sensitivity of 92.3% for correctly identifying patients with objective cognitive impairment and 81.5% for identifying those with subjective impairments at 6 months. Although the s-MoCA-SWE had higher sensitivity, the MoCA had a more balanced sensitivity and specificity in detecting both subjective and objective cognitive impairments. In both crude and multivariable models, the s-MoCA-SWE was more strongly associated than the MoCA with cognitive impairment at 6 months. CONCLUSIONS: Both the short and standard versions of the MoCA appear to be effective in identifying individuals likely to experience persistent cognitive issues following a stroke. Considering the limited time available in an acute stroke unit, the short-form version may be more practical. Nevertheless, further prospective studies are required to validate these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".