Comparison of the Mini‐Mental State Examination and Montreal Cognitive Assessment executive subtests in detecting post‐stroke cognitive impairment
Bibliographic record
Abstract
AIM: The Montreal Cognitive Assessment (MoCA) has been shown to be more sensitive in detecting executive dysfunction than the Mini-Mental State Examination (MMSE). However, it is still not known whether all the MoCA executive subtests contribute to the superior sensitivity. Thus, the present study aimed to determine how much executive abnormality was detected by the MMSE and MoCA executive subtests in a population-based cohort of Chinese post-stroke patients. METHODS: The MMSE and MoCA were collected from post-stroke patients (within 15 days to 1 month after stroke, including ischemic stroke and hemorrhagic stroke) in 14 hospitals of northern and southern China (including 10 top-graded hospitals and 4 community hospitals) between June 2011 and September 2013. The proportions of patients with incorrect MoCA executive subtests and the proportions of patients with incorrect MMSE executive subtests were compared. RESULTS: A total of 1222 patients (703 men and 519 women, aged 62.06 ± 10.68 and 62.76 ± 9.86 years, respectively) were recruited. The MoCA detected more patients with executive dysfunction than the MMSE (OR 15.399, 95% CI 12.631-18.773; P < 0.001). The likelihood of incorrect MMSE executive tasks increased across decreasing scores of MoCA executive tasks (P < 0.001 for trend). Compared with the MMSE three-step command test (15.5%), the MoCA trail-making (57.8%), abstraction (48.0%) and abstraction (measurement tool; 45.7%) detected more patients with executive dysfunction (P < 0.001), whereas the MoCA digit span forwards (4.3%) and backwards (11.6%) detected fewer patients (P < 0.001 and P = 0.005, respectively). CONCLUSIONS: The MoCA executive tasks are more sensitive in detecting executive dysfunction compared with the MMSE executive tasks. Geriatr Gerontol Int 2017; 17: 2329-2335.
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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 | 0.008 |
| 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 | 0.001 | 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".