P1‐470: Validity study of the Montreal cognitive assessment in patients with vascular dementia
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) is a brief cognitive screening toll for detecting cognitive decline in older people. The MoCA evaluates a large number of cognitive areas and tasks are more complex, as compared with the Mini-Mental State Examination (MMSE; Folstein et al., 1975), which makes it a more sensitive instrument in the discrimination between cognitive decline and normal aging. This work is a validity study of the Montreal Cognitive Assessment (MoCA) in patients with Vascular Dementia (VaD). We evaluated the psychometric properties, convergent validity (with MMSE) and sensitivity of the both instruments to detect the VaD patients. A clinical group of VaD (n = 32) were recruited at the Dementia Clinic, Neurology Department of Coimbra University Hospital. The diagnosis was previously established based on international criteria for the diagnosis of VaD (Roman et al., 1993). The control group (n = 32) was extracted from the sample of the normative study of MoCA to Portuguese population (Freitas et al., 2011). All participants were evaluated with both the MMSE and MoCA. The Cronbach's alpha of MoCA as an index of internal consistency is .899 (MMSE: Cronbach's alpha = .793). The MoCA score is highly correlated with MMSE score (r = .764, p < .01). There are no significant between group differences in gender (t = .532, p = .597), age (t = .152, p = .880) and education (t = .189, p = .851). There are statistically significant between group differences in mean score of both instruments (MoCA: t = 10.687, p < .001; MMSE: t = 5.269, p < .001). These differences are more pronounced in MoCA scores (MoCA_Control: 22.56 ± 3.379; MoCA_VaD: 12.38 ± 4.203; MoCA_MeanDifference: 10.188; MMSE_Control: 27.94 ± 1.435; MMSE_VaD: 23.91 ± 4.083; MMSE_MeanDifference: 4.031). Considering the normative data for Portuguese population (Freitas et al., 2011) and the criterion of 2 SD below of the mean, MoCA shows a high sensitivity in identifying VaD patients (84.4%) unlike the MMSE proved to be very poor (28.1%; Portuguese cut-off points: Guerreiro, 1998). Comparing with MMSE, the MoCA is a better toll for brief cognitive screening of VaD patients, showing better psychometric properties and a significantly stronger ability to differentiate the grey area of normality. Therefore, MoCA should be the chosen instrument for screening VaD patients.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".