Sensitivity and specificity of the Montreal cognitive assessment using U.S. National alzheimer coordinating centre uniform data set: a retrospective analysis of 16,309 participants
Bibliographic record
Abstract
BACKGROUND: Neurodegenerative diseases (NDDs), like Alzheimer's disease, are characterized by progressive cognitive decline, with limited effective treatments available. Several screening tools are available for diagnosing various types of dementia, including the Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE), and the Dementia Rating Scale (DRS). OBJECTIVE: This study aims to evaluate the sensitivity and specificity of MoCA to determine its suitability as a screening tool. METHODS: This study analyzed data from participants aged 55 and older, recruited from U.S. Alzheimer's Disease Research Centers (ADRCs), using a National Alzheimer Coordinating Center Uniformed Data Set (NACC-UDS). Participants were classified based on patient records into demented and non-demented groups, with the non-demented group further categorized into those with normal cognition and cognitive impairment (CI). This analysis examines the correlation between these classifications and MoCA scores. RESULTS: This study utilized an initial dataset of 188,700 participant records from NACC. After applying inclusion criteria, 16,309 participants were included. The participants had complete diagnostic information, clinician-conducted cognitive assessments, and MoCA scores. The participants were categorized into three groups: 7,624 with no cognitive impairment (NoCI), 4,893 with CI, and 3,792 with dementia. This study focused on MoCA scores, revealing significant differences among diagnostic groups. ROC analysis demonstrated the MoCA's strong diagnostic capability, with AUC values significantly above 0.5 (P <.001). Sensitivity and specificity were calculated in at the literature-recommended cutoff scores of 26 and 21, while the optimal cutoff scores were identified as (< 24) for detecting MCI and (< 21) for dementia based on the Youden index in reference to individuals with no cognitive impairment. Although PPV was generally low, the high NPV across comparisons underscores the MoCA's effectiveness in ruling out cognitive impairment. CONCLUSION: The study confirms MoCA as an effective tool for detecting dementia, showing 83% sensitivity and 82% specificity at a cutoff value of 21. With a high NPV of 94%, MoCA is particularly reliable for ruling out dementia. Its ability to detect MCI is moderate, with a sensitivity of 77.3% at cutoff of 24 among normal population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".