Montreal Cognitive Assessment as Screening Measure for Mild and Major Neurocognitive Disorder in a Chilean Population
Bibliographic record
Abstract
<b><i>Background:</i></b> The Montreal Cognitive Assessment (MoCA) is a sensitive screening instrument for mild neurocognitive disorder (mild NCD). However, cut-off scores and accuracy indices should be established using representative samples of the population. In this context, the aim of this study was to update the normative values, and diagnostic efficiency statistics of the MoCA to detect mild NCD in the Chilean population. <b><i>Methods:</i></b> This study included 226 participants from the north, center, and south of the country, classified into 3 groups: healthy elderly (HE; <i>n</i> = 113), mild NCD (<i>n</i> = 65), and major neurocognitive disorder (major NCD; <i>n</i> = 48). <b><i>Results:</i></b> The optimal cut-off score to discriminate mild NCD from HE participants was 20 points with a sensitivity of 82.8% and a specificity of 84.1%. The observed balance between sensitivity and specificity shows a good test performance either to confirm or discard a diagnosis. The cut-off between mild NCD and major NCD from HE participants was 19 points with 85.6% of sensitivity and 90.3% of specificity. <b><i>Conclusion:</i></b> Overall diagnostic accuracy can be considered as outstanding (AUC ≥0.904) when discriminating HE from both mild NCD and major NCD. These results showed that the MoCA is a suitable tool to identify mild NCD and major NCD.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".